├── .gitignore ├── 0_resources ├── 1502.05767.pdf ├── NA-08-01.pdf ├── README.md ├── adjoint.pdf └── deeplearning2017_johnson_automatic_differentiation_01.pdf ├── 1_deep_learning ├── README.md ├── brachistochrone │ ├── README.md │ ├── brachistochrone.py │ └── exact_solver.py ├── computation_graph.py ├── lecture_notes.pdf ├── realnvp │ ├── README.md │ ├── main.py │ ├── objectives │ │ ├── __init__.py │ │ ├── gaussian.py │ │ ├── mog.py │ │ ├── ring2ds.py │ │ ├── template.py │ │ └── wave.py │ └── realnvp.py ├── schrodinger.py ├── simple_ad.py ├── slides │ ├── SSSS-1-DLReview.pdf │ ├── SSSS-2-GenerativeModels.pdf │ ├── SSSS-3-DiffProgm.pdf │ └── SSSS-4-DLApplication.pdf └── tanh.py ├── 2_tensor_network ├── README.md ├── Tutorial_tensor_network.pdf ├── assets │ ├── Z.png │ ├── Z2.png │ ├── gen1.png │ ├── gen2.png │ ├── gen3.png │ ├── gen4.png │ ├── gradients.png │ ├── image-20190430141119642.png │ ├── image-20190430143433180.png │ ├── image-20190430144239877.png │ ├── image-20190430144449213.png │ ├── image-20190430145634644.png │ ├── image-20190430150813001.png │ ├── image-20190430152031663.png │ ├── init.png │ ├── init2.png │ └── px.png ├── imgs │ ├── L.png │ ├── L0.png │ ├── Z.png │ ├── born_machine.png │ ├── cond_prob.png │ ├── cp.png │ ├── joint_prob.png │ ├── mnist_mps.png │ ├── mps.png │ ├── mps1.png │ ├── mps2.png │ ├── mps_left.png │ ├── psi_prime.png │ ├── rank_one.png │ ├── recon0.png │ ├── recon1.png │ ├── supunsup.png │ ├── tensor.png │ ├── tensor_diagram.png │ ├── tensor_networks.png │ ├── training.png │ ├── tucker.png │ └── two_qubits.png ├── kacward.py ├── mnist ├── mnist28.npy ├── mnist784_bin_1000.npy ├── mnist_100_28x28_p0.5.npy ├── mps_tutorial.ipynb ├── tensor_contraction_methods.pdf └── tensor_contraction_simple.ipynb ├── 3_julia └── julia-hands-on.ipynb ├── 4_quantum ├── QC-with-Yao.ipynb ├── README.md ├── VQE_action.ipynb ├── bloch_sphere.jl ├── graph_embeding.ipynb ├── images │ ├── diff_circuit.png │ ├── differentiable.png │ ├── fourqubit.png │ ├── hgate.png │ ├── landscape.pdf │ ├── mreset.png │ ├── qfttn.png │ ├── twoqubit.png │ └── twoqubit.py ├── kernel_learn.ipynb ├── qc_tensor_mapping.ipynb ├── qcbm_gaussian.ipynb ├── quantum_lecture_note.pdf ├── variational_quantum_circuit.ipynb └── yao-talk-2019.pdf ├── Challenge.md ├── LICENSE ├── Manifest.toml ├── Project.toml ├── README.md ├── _assets ├── SongShanHu2019.jpeg ├── SongShanHu2019.key └── c60.jpg └── src └── SSSS.jl /.gitignore: -------------------------------------------------------------------------------- 1 | *.jl.cov 2 | *.jl.*.cov 3 | *.jl.mem 4 | *.jl~ 5 | *~ 6 | .DS_Store 7 | 8 | *.jl.cov 9 | *.jl.*.cov 10 | *.jl.mem 11 | 12 | docs/build/ 13 | docs/site/ 14 | 15 | _local/ 16 | .ipynb_checkpoints/ 17 | .vscode/ 18 | *.ipynb_checkpoints 19 | **/*.ipynb_checkpoints 20 | **/**/*.ipynb_checkpoints 21 | *.out 22 | *.log 23 | 24 | *.jld2 25 | **/*.jld2 26 | **/**/*.jld2 27 | 28 | *.jld 29 | **/*.jld 30 | **/**/*.jld 31 | 32 | _*.dat 33 | *.swp 34 | __pycache__/ 35 | 36 | *.aux 37 | -------------------------------------------------------------------------------- /0_resources/1502.05767.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/0_resources/1502.05767.pdf -------------------------------------------------------------------------------- /0_resources/NA-08-01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/0_resources/NA-08-01.pdf -------------------------------------------------------------------------------- /0_resources/README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## Automatic Differentiation 4 | 5 | ### Theory 6 | 7 | https://timvieira.github.io/blog/post/2017/08/18/backprop-is-not-just-the-chain-rule/ 8 | 9 | https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf 10 | 11 | https://math.mit.edu/~stevenj/18.336/adjoint.pdf 12 | 13 | https://colah.github.io/posts/2015-08-Backprop/ 14 | 15 | http://www.cs.cornell.edu/courses/cs5740/2017sp/lectures/04-nn-compgraph.pdf 16 | 17 | ### Implementation 18 | 19 | http://videolectures.net/deeplearning2017_johnson_automatic_differentiation/ 20 | 21 | https://github.com/mattjj/autodidact 22 | 23 | ### Applications 24 | 25 | https://arxiv.org/abs/1502.05767 26 | 27 | ## Generative Models 28 | 29 | https://openai.com/blog/generative-models/ 30 | 31 | https://deepmind.com/blog/wavenet-generative-model-raw-audio/ 32 | 33 | https://deepmind.com/blog/high-fidelity-speech-synthesis-wavenet/ 34 | 35 | https://deepgenerativemodels.github.io/notes/index.html 36 | 37 | ## Probabilistic Graphical Models 38 | 39 | https://ermongroup.github.io/cs228-notes/ 40 | 41 | ## Differentiable Programming 42 | 43 | https://www.edge.org/response-detail/26794 44 | 45 | https://medium.com/@karpathy/software-2-0-a64152b37c35 46 | 47 | https://www.youtube.com/watch?v=LjWzgTPFu14 48 | 49 | ## Neural ODE 50 | 51 | https://github.com/rtqichen/torchdiffeq 52 | 53 | https://news.ycombinator.com/item?id=18676986 54 | 55 | ## Deep Learning with PyTorch 56 | 57 | https://fleuret.org/ee559/ 58 | 59 | 60 | -------------------------------------------------------------------------------- /0_resources/adjoint.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/0_resources/adjoint.pdf -------------------------------------------------------------------------------- /0_resources/deeplearning2017_johnson_automatic_differentiation_01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/0_resources/deeplearning2017_johnson_automatic_differentiation_01.pdf -------------------------------------------------------------------------------- /1_deep_learning/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | ## Table of Contents 4 | * [`lecture_notes.pdf`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/lecture_notes.pdf) and [`slides/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/slides) 5 | * Demo codes 6 | * Poor man's computation graph: [`computation_graph.py`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/computation_graph.py) 7 | * Variational free energy with flow model: [`realnvp/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/realnvp) 8 | * Hamiltonian inverse design with reverse mode AD: [`schrodinger.py`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/schrodinger.py) 9 | * Solving the fastest descent problem with NeuralODE [`brachistochrone/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/brachistochrone) 10 | 11 | 12 | Welcome for pull requests and issues! 13 | 14 | -------------------------------------------------------------------------------- /1_deep_learning/brachistochrone/README.md: -------------------------------------------------------------------------------- 1 | ## Solves the brachistochrone problem with [NeuralODE](https://github.com/rtqichen/torchdiffeq) 2 | 3 | Do you still remember the "quickest descent" problem we learned as a kid ? If not, please [remind yourself](http://mathworld.wolfram.com/BrachistochroneProblem.html) a bit. 4 | 5 | Here, we are going to solve the problem by minimizing the path parametrized by a neural network. The unusual thing is that our objective function involves an integration 6 | $$ 7 | t = \int_{x_0}^{x_1} \sqrt{\frac{1+(dy/dx)^2}{2 g (y_1- y_0) + v_0^2}} d x 8 | $$ 9 | We assume the particle moves from $(x_0, y_0)=(0, 0)$ to $(x_1, y_1)$ along the path $y(x)$ . And $g$ is the gravity constant, $v_0$ is the initial velocity. 10 | 11 | Computing the objective function amounts to integrating an ordinary differential equation. And NeuralODE computes its gradient with respect to path, accurately and efficiently! 12 | 13 | Play with the code and think about the following 14 | 15 | - [ ] Change the value of $g$ and $v_0$. Does the solution agrees with your intuition ? 16 | - [ ] What happens when $v_0\rightarrow 0$ ? Why does this happen ? Could you fix it ? 17 | - [ ] Is there any other cool application you can thing of ? You are welcome to share with us. 18 | 19 | -------------------------------------------------------------------------------- /1_deep_learning/brachistochrone/brachistochrone.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import torch.optim as optim 5 | import numpy as np 6 | 7 | from torchdiffeq import odeint_adjoint as odeint 8 | from exact_solver import solver 9 | 10 | class MLP(nn.Module): 11 | def __init__(self, hidden_size, y1=1.0): 12 | super(MLP, self).__init__() 13 | self.fc1 = nn.Linear(1, hidden_size) 14 | self.fc2 = nn.Linear(hidden_size, hidden_size) 15 | self.fc3 = nn.Linear(hidden_size, 1) 16 | 17 | self.y1 = y1 18 | 19 | def _f(self, x): 20 | out = F.softplus(self.fc1(x)) 21 | out = F.softplus(self.fc2(out)) 22 | out = self.fc3(out) 23 | return out.sum() 24 | 25 | def forward(self, x): 26 | ''' 27 | y(0) = 0 28 | y(1) = y1 29 | ''' 30 | f0 = self._f(torch.tensor([0.0])) 31 | f1 = self._f(torch.tensor([1.0])) 32 | return self._f(x) - (f0 + self.y1)*(1.0-x) - f1*x + self.y1 33 | 34 | def value_and_grad(self, x): 35 | y = self.forward(x) 36 | return y, torch.autograd.grad(y, x, grad_outputs=torch.ones(x.shape[0]), create_graph=True)[0] 37 | 38 | class Brachistochrone(nn.Module): 39 | def __init__(self, g, v0, net): 40 | super(Brachistochrone, self).__init__() 41 | self.v0 = v0 42 | self.g = g 43 | self.net = net 44 | 45 | def forward(self, x, t): 46 | with torch.enable_grad(): 47 | y, dydx = self.net.value_and_grad(x.view(-1).detach().requires_grad_()) 48 | return torch.sqrt((1+dydx**2)/(2*self.g*y+ self.v0**2)) 49 | 50 | def plot(model,para): 51 | plt.cla() 52 | xlist = torch.linspace(0.0, 1.0, 21) 53 | ylist = [model.net(torch.tensor([x])) for x in xlist] 54 | plt.plot(xlist.numpy(), ylist, lw=2,label='learned curve') 55 | plt.plot([0.0, 1.0], [0.0, model.net.y1], 'r*', ms=20) 56 | plt.gca().invert_yaxis() 57 | 58 | tlist = np.linspace(para[2],para[3],21) 59 | xlist = para[0]*(tlist- np.sin(tlist)) - para[1] 60 | ylist = para[0]*(1 - np.cos(tlist)) -para[4] 61 | plt.plot(xlist,ylist,lw=2,label='exact') 62 | plt.legend(loc='upper right') 63 | 64 | plt.xlabel('$x$') 65 | plt.ylabel('$y$') 66 | 67 | plt.draw() 68 | plt.pause(0.01) 69 | 70 | if __name__ == '__main__': 71 | 72 | g = 10.0 #gravity 73 | v0 = 1.0 #initial velocity 74 | nh = 32 #number of hidden neurons 75 | y1 = 1.0 #fininal y coordinate 76 | 77 | para = np.append(solver(v0,g,y1),v0**2/(2*g)) # exact solution as a reference 78 | tbest = (para[3]-para[2])*np.sqrt(para[0]/g) 79 | 80 | model = Brachistochrone(g, v0, MLP(nh, y1)) 81 | optimizer = optim.Adam(model.parameters(), lr=1E-2) 82 | 83 | import matplotlib.pyplot as plt 84 | # Set up figure. 85 | fig = plt.figure(figsize=(8,8), facecolor='white') 86 | ax = fig.add_subplot(111, frameon=False) 87 | plt.ion() 88 | plt.show(block=False) 89 | 90 | for epoch in range(100): 91 | optimizer.zero_grad() 92 | t = odeint(model, torch.tensor([0.0]), torch.tensor([0.0, 1.0])) 93 | loss = t[1] - t[0] 94 | loss.backward() 95 | optimizer.step() 96 | print ("step = %d"%epoch, "time = %.5f"%loss.item(), " (exact = %.5f)"%tbest) 97 | plot(model,para) 98 | -------------------------------------------------------------------------------- /1_deep_learning/brachistochrone/exact_solver.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | def solver(v0,g,y1): 3 | #'''the number of points to draw 4 | # y = k(1-cos(theta)) 5 | # x = k(theta- sin(theta)) 6 | # Point 1:(-b,-v0**2/(2*g)) theta=0 7 | # Point 2:(0,0) theta0 8 | # Point 3:(1,1) theta1''' 9 | a = v0**2.0/(2*g) 10 | def f(para): 11 | [k,b,theta0,theta1] = para 12 | out = np.zeros(4) 13 | out[0] = k*(1-np.cos(theta0))-a 14 | out[1] = k*(theta0 - np.sin(theta0))-b 15 | out[2] = k*(1-np.cos(theta1))-y1-a 16 | out[3] = k*(theta1 - np.sin(theta1))-1-b 17 | return np.array(out) 18 | from scipy.optimize import fsolve 19 | return fsolve(f,[2.0, 0.5 , 0.5 , 2.0]) #start point 20 | 21 | if __name__ == "__main__": 22 | y=1.0 23 | v0=0 24 | g=10 25 | para = solver(v0,g,y) 26 | para = np.append(para,v0**2.0/(2*g)) 27 | theta_list = np.linspace(para[2],para[3],3) 28 | x_exact = para[0]*(theta_list-np.sin(theta_list))-para[1] 29 | y_exact = para[0]*(1-np.cos(theta_list))-para[4] 30 | print(para) 31 | print(theta_list) 32 | print(x_exact) 33 | print(y_exact) 34 | -------------------------------------------------------------------------------- /1_deep_learning/computation_graph.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | import numpy as np 3 | 4 | class Dense(object): 5 | ''' 6 | linear node f(x) = xW + b. 7 | 8 | Attributes: 9 | params (list): variables (input nodes) that directly feed into this node, W and b. 10 | params_delta (list): gradients for parameters. 11 | ''' 12 | def __init__(self, input_shape, output_shape, mean=0, variance=0.01): 13 | self.params = [mean + variance * np.random.randn(input_shape, output_shape), 14 | mean + variance * np.random.randn(output_shape)] 15 | self.params_delta = [None, None] 16 | 17 | def forward(self, x, *args): 18 | '''function itself.''' 19 | self.x = x # store for backward 20 | W, b = self.params 21 | return np.dot(x, W) + b 22 | 23 | def backward(self, delta): 24 | ''' 25 | Args: 26 | delta (ndarray): gradient of L with repect to node's output, dL/dy. 27 | 28 | Returns: 29 | ndarray: gradient of L with respect to node's input, dL/dx 30 | ''' 31 | self.params_delta[0] = np.dot(self.x.T, delta) 32 | self.params_delta[1] = np.sum(delta, 0) 33 | return np.dot(delta, self.params[0].T) 34 | 35 | class F(object): 36 | '''base class for functions with no parameters.''' 37 | def __init__(self): 38 | self.params = [] 39 | self.params_delta = [] 40 | 41 | class Sigmoid(F): 42 | '''Sigmoid activation function module''' 43 | def forward(self, x): 44 | self.y = 1.0 / (1.0 + np.exp(-x)) 45 | return self.y 46 | 47 | def backward(self, delta): 48 | return delta * ((1 - self.y) * self.y) 49 | 50 | class MSE(F): 51 | '''Mean function module''' 52 | def __init__(self, y): 53 | super(MSE, self).__init__() 54 | self.y = y 55 | 56 | def forward(self, x): 57 | self.x = x 58 | return ((x-self.y)**2).mean() 59 | 60 | def backward(self, delta): 61 | return delta*2*(self.x-self.y)/np.prod(self.x.shape) 62 | 63 | class Sequential(object): 64 | def __init__(self, layers): 65 | self.layers = layers 66 | 67 | def forward(self, x): 68 | for l in self.layers: 69 | x = l.forward(x) 70 | return x 71 | 72 | def backward(self): 73 | delta = 1.0 74 | for l in self.layers[::-1]: 75 | delta = l.backward(delta) 76 | return delta 77 | 78 | if __name__=='__main__': 79 | np.random.seed(42) 80 | 81 | n_batch = 32 82 | n_in = 1 83 | n_hidden = 100 84 | 85 | x = np.random.rand(n_batch, n_in) 86 | y = (x**2).sum(axis=1, keepdims=True) # size = (n_batch, 1) 87 | 88 | model = Sequential([Dense(n_in, n_hidden), Sigmoid(), Dense(n_hidden, 1), MSE(y)]) 89 | 90 | def func(x): 91 | x = x.reshape(n_batch, n_in) 92 | return model.forward(x) 93 | 94 | def grad(x): 95 | x = x.reshape(n_batch, n_in) 96 | model.forward(x) 97 | return model.backward().reshape(n_batch*n_in) 98 | 99 | from scipy.optimize import check_grad 100 | x = np.random.randn(n_batch*n_in) 101 | print ('gradient check:', check_grad(func, grad, x) ) 102 | -------------------------------------------------------------------------------- /1_deep_learning/lecture_notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/1_deep_learning/lecture_notes.pdf -------------------------------------------------------------------------------- /1_deep_learning/realnvp/README.md: -------------------------------------------------------------------------------- 1 | # Fun with Normalizing Flows 2 | 3 | Normalizing Flows are an iterative flows from the latent space of simple *base distribution* (e.g. independent Gaussians) to the data space with complex distributions. These flows are reversible, which means that you can use them to map complex data distribution to the normal distribution, hence the name **Normalizing Flow**. Normalizing Flows are simple yet elegant generative models which demonstrate **representation learning**. 4 | 5 | Some background readings before start: 6 | 7 | - Rui Shu's [Precursor to Normalizing Flows](http://ruishu.io/2018/05/19/change-of-variables/) 8 | 9 | - Eric Jang's tutorial [1](https://blog.evjang.com/2018/01/nf1.html) and [2](https://blog.evjang.com/2018/01/nf2.html) 10 | 11 | - OpenAI's [Glow](https://blog.openai.com/glow/) 12 | 13 | Here we employ the Real NVP network introduced in [this paper](https://arxiv.org/abs/1605.08803) for variational calculation of toy target densities. The goal is to minimize the following loss 14 | $$ 15 | \mathcal{L} = \int d x\, q(x) [\ln q(x) + E (x)], 16 | $$ 17 | where $q(x)$ is the model density, and $E(x)$ is a given energy function. One can show that the loss function is lower bounded $\mathcal{L} \ge -\ln Z$, where $Z = \int d x \, e^{-E(x)}$ is the partition function. One will arrive at the equality only when the variational density matches to the target density $q(x) = e^{-E(x)}/Z$. 18 | 19 | Please play with the code and finish the following tasks 20 | 21 | - [ ] Make a plot of the loss versus training epochs, and compare with exactly computed $-\ln Z$ 22 | - [ ] How to make sense of the learned latent space ? Could you do something fun with it ? 23 | 24 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/main.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | 7 | from realnvp import NVPNet 8 | import objectives 9 | 10 | if __name__=='__main__': 11 | import argparse 12 | parser = argparse.ArgumentParser(description='') 13 | parser.add_argument("-cuda", type=int, default=-1, help="use GPU") 14 | parser.add_argument("-target", default='Ring2D', 15 | choices=['Ring2D', 'Ring5', 'Wave', 'Gaussian', 'Mog2'], help="target distribution") 16 | parser.add_argument("-batchsize", type=int, default=1024, help="batchsize") 17 | args = parser.parse_args() 18 | device = torch.device("cpu" if args.cuda<0 else "cuda:"+str(args.cuda)) 19 | 20 | xlimits=[-4, 4] 21 | ylimits=[-4, 4] 22 | numticks=31 23 | x = np.linspace(*xlimits, num=numticks, dtype=np.float32) 24 | y = np.linspace(*ylimits, num=numticks, dtype=np.float32) 25 | X, Y = np.meshgrid(x, y) 26 | xy = np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T 27 | xy = torch.from_numpy(xy).contiguous().to(device) 28 | 29 | # Set up plotting code 30 | def plot_isocontours(ax, func, alpha=1.0): 31 | zs = np.exp(func(xy).cpu().detach().numpy()) 32 | Z = zs.reshape(X.shape) 33 | plt.contour(X, Y, Z, alpha=alpha) 34 | ax.set_yticks([]) 35 | ax.set_xticks([]) 36 | plt.xlim(xlimits) 37 | plt.ylim(ylimits) 38 | 39 | target = getattr(objectives, args.target)() 40 | target.to(device) 41 | 42 | # Set up figure. 43 | fig = plt.figure(figsize=(8,8), facecolor='white') 44 | ax = fig.add_subplot(111, frameon=False) 45 | plt.ion() 46 | plt.show(block=False) 47 | 48 | model = NVPNet(dim = 2, hdim = 10, depth = 8) 49 | model.to(device) 50 | 51 | optimizer = torch.optim.Adam(model.parameters(), lr = 1e-2) 52 | 53 | params = list(model.parameters()) 54 | params = list(filter(lambda p: p.requires_grad, params)) 55 | nparams = sum([np.prod(p.size()) for p in params]) 56 | print ('total nubmer of trainable parameters:', nparams) 57 | 58 | np_losses = [] 59 | for e in range(200): 60 | x, logp = model.sample(args.batchsize) 61 | loss = logp.mean() - target(x).mean() 62 | 63 | model.zero_grad() 64 | loss.backward() 65 | optimizer.step() 66 | 67 | with torch.no_grad(): 68 | print (e, loss.item()) 69 | np_losses.append([loss.item()]) 70 | 71 | plt.cla() 72 | plot_isocontours(ax, target, alpha=0.5) 73 | plot_isocontours(ax, model.logprob) 74 | 75 | samples = x.cpu().detach().numpy() 76 | plt.plot(samples[:, 0], samples[:,1],'o', alpha=0.8) 77 | 78 | plt.draw() 79 | plt.pause(0.01) 80 | 81 | np_losses = np.array(np_losses) 82 | fig = plt.figure(figsize=(8,8), facecolor='white') 83 | plt.ioff() 84 | plt.plot(np_losses) 85 | plt.show() 86 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/__init__.py: -------------------------------------------------------------------------------- 1 | from .ring2ds import Ring2D,Ring5 2 | from .mog import Mog2 3 | from .wave import Wave 4 | from .gaussian import Gaussian 5 | 6 | __all__ =['Ring2D','Ring5','Mog2','Gaussian', 'Wave'] 7 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/gaussian.py: -------------------------------------------------------------------------------- 1 | from .template import Target 2 | 3 | class Gaussian(Target): 4 | def __init__(self): 5 | super(Gaussian, self).__init__(2,'Gaussian') 6 | 7 | def energy(self, x): 8 | return (-x[:,0]**2 - 2*x[:, 1]**2 - 2.0*x[:, 0] * x[:, 1]) 9 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/mog.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from .template import Target 4 | 5 | class Mog2(Target): 6 | 7 | def __init__(self, offset=0.8): 8 | super(Mog2, self).__init__(2,'Mog2') 9 | self.offset = offset 10 | 11 | def energy(self, x): 12 | 13 | v1 = torch.sqrt((x[:,0]-self.offset)**2 + (x[:, 1]-self.offset)**2)*2. 14 | v2 = torch.sqrt((x[:,0]+self.offset)**2 + (x[:, 1]+self.offset)**2)*2. 15 | 16 | pdf1 = torch.exp(-0.5* v1*v1) /np.sqrt(2*np.pi * 0.25) 17 | pdf2 = torch.exp(-0.5* v2*v2) /np.sqrt(2*np.pi * 0.25) 18 | 19 | return torch.log(0.5*pdf1 + 0.5* pdf2) 20 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/ring2ds.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from .template import Target 4 | 5 | class Ring2D(Target): 6 | 7 | def __init__(self): 8 | super(Ring2D, self).__init__(2,'Ring2D') 9 | 10 | def energy(self, x): 11 | return -(torch.sqrt((x**2).sum(dim=1))-2.0)**2/0.32 12 | 13 | class Ring5(Target): 14 | 15 | def __init__(self): 16 | super(Ring5, self).__init__(2,'Ring5') 17 | 18 | def energy(self, x): 19 | x2 = torch.sqrt((x**2).sum(dim=1)) 20 | u1 = (x2 - 1.) **2 /0.04 21 | u2 = (x2 - 2.) **2 /0.04 22 | u3 = (x2 - 3.) **2 /0.04 23 | u4 = (x2 - 4.) **2 /0.04 24 | u5 = (x2 - 5.) **2 /0.04 25 | 26 | u1 = u1.view(-1, 1) 27 | u2 = u2.view(-1, 1) 28 | u3 = u3.view(-1, 1) 29 | u4 = u4.view(-1, 1) 30 | u5 = u5.view(-1, 1) 31 | 32 | u = torch.cat((u1, u2, u3, u4, u5), dim=1) 33 | return -torch.min(u, dim=1)[0] -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/template.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | class Target(nn.Module): 5 | ''' 6 | base class for target 7 | ''' 8 | def __init__(self,nvars,name = "Target"): 9 | super(Target, self).__init__() 10 | self.nvars = nvars 11 | self.name = name 12 | 13 | def __call__(self, x): 14 | return self.energy(x) 15 | 16 | def energy(self,z): 17 | raise NotImplementedError(str(type(self))) 18 | -------------------------------------------------------------------------------- /1_deep_learning/realnvp/objectives/wave.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from .template import Target 4 | 5 | class Wave(Target): 6 | 7 | def __init__(self): 8 | super(Wave, self).__init__(2,'Wave') 9 | 10 | def energy(self, x): 11 | w = torch.sin(np.pi*x[:, 0]/2.) 12 | return -0.5*((x[:, 1] -w)/0.4)**2 -------------------------------------------------------------------------------- /1_deep_learning/realnvp/realnvp.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | 5 | class NVPCouplingLayer(nn.Module): 6 | 7 | def __init__(self, map_s, map_t, b): 8 | super(NVPCouplingLayer , self).__init__() 9 | self.map_s = map_s 10 | self.map_t = map_t 11 | self.b = b.clone().unsqueeze(0) 12 | 13 | def forward(self, x): 14 | self.logjac = x.new_zeros(x.shape[0]) 15 | s, t = self.map_s(self.b * x), self.map_t(self.b * x) 16 | y = self.b * x + (1 - self.b) * (torch.exp(s) * x + t) 17 | self.logjac += ((1 - self.b) * s).sum(1) 18 | return y 19 | 20 | def inverse(self, y): 21 | self.logjac = y.new_zeros(y.shape[0]) 22 | s, t = self.map_s(self.b * y), self.map_t(self.b * y) 23 | self.logjac -= ((1 - self.b) * s).sum(1) 24 | y = self.b * y + (1 - self.b) * (torch.exp(-s) * (y - t)) 25 | return y 26 | 27 | class NVPNet(nn.Module): 28 | def __init__(self, dim, hdim, depth, device='cpu'): 29 | super(NVPNet, self).__init__() 30 | self.dim = dim 31 | self.device = device 32 | b = torch.Tensor(dim).to(device) 33 | self.layers = nn.ModuleList() 34 | for d in range(depth): 35 | if d%2 == 0: 36 | # Tag half the dimensions 37 | i = torch.randperm(b.numel()).narrow(0, 0, b.numel() // 2) 38 | b.zero_()[i] = 1 39 | else: 40 | b=1-b 41 | 42 | map_s = nn.Sequential(nn.Linear(dim, hdim), 43 | nn.ELU(), 44 | nn.Linear(hdim, hdim), 45 | nn.ELU(), 46 | nn.Linear(hdim, dim) 47 | ) 48 | map_t = nn.Sequential(nn.Linear(dim, hdim), 49 | nn.ELU(), 50 | nn.Linear(hdim, hdim), 51 | nn.ELU(), 52 | nn.Linear(hdim, dim) 53 | ) 54 | self.layers.append(NVPCouplingLayer(map_s, map_t, b)) 55 | 56 | def forward(self, x): 57 | self.logjac = x.new_zeros(x.shape[0]) 58 | for m in self.layers: 59 | x = m(x) 60 | self.logjac += m.logjac 61 | return x 62 | 63 | def inverse(self, y): 64 | self.logjac = y.new_zeros(y.shape[0]) 65 | for m in reversed(self.layers): 66 | y = m.inverse(y) 67 | self.logjac += m.logjac 68 | return y 69 | 70 | def sample(self, batch_size): 71 | z = torch.Tensor(batch_size, self.dim).normal_() 72 | x = self.forward(z) 73 | logp = - 0.5 * z.pow(2).add(math.log(2 * math.pi)).sum(1) - self.logjac 74 | return x, logp 75 | 76 | def logprob(self, x): 77 | z = self.inverse(x) 78 | return - 0.5 * z.pow(2).add(math.log(2 * math.pi)).sum(1) + self.logjac 79 | 80 | def save(self, save_dict): 81 | for d, layer in enumerate(self.layers): 82 | save_dict['map_s'+str(d)] = layer.map_s.state_dict() 83 | save_dict['map_t'+str(d)] = layer.map_t.state_dict() 84 | save_dict['mask'+str(d)] = layer.b.to('cpu') 85 | return save_dict 86 | 87 | def load(self, save_dict): 88 | for d, layer in enumerate(self.layers): 89 | layer.map_s.load_state_dict(save_dict['map_s'+str(d)]) 90 | layer.map_t.load_state_dict(save_dict['map_t'+str(d)]) 91 | layer.b = save_dict['mask'+str(d)].to(self.device) 92 | return save_dict 93 | 94 | if __name__=='__main__': 95 | 96 | batch_size = 100 97 | dim = 2 98 | 99 | model = NVPNet(dim = dim, hdim = 4, depth = 8) 100 | z = torch.randn(batch_size, dim, requires_grad=True) 101 | 102 | x = model.forward(z) # generate a new dataset. 103 | x_logjac = model.logjac # record log(Jacobian) in generate process. 104 | print (model.logjac) 105 | 106 | z_infer = model.inverse(x) # inference back to the original dataset. 107 | z_infer_logjac = model.logjac # record log(Jacobian) in inference process. 108 | print (model.logjac) 109 | 110 | from numpy.testing import assert_array_almost_equal 111 | assert_array_almost_equal(z_infer.data.numpy(),z.data.numpy()) # test if they are the same. 112 | assert_array_almost_equal(x_logjac.data.numpy(),-z_infer_logjac.data.numpy()) # abs(log(Jacobian)) 113 | -------------------------------------------------------------------------------- /1_deep_learning/schrodinger.py: -------------------------------------------------------------------------------- 1 | ''' 2 | idea taken from https://math.mit.edu/~stevenj/18.336/adjoint.pdf 3 | ''' 4 | 5 | import numpy as np 6 | import torch 7 | torch.set_default_dtype(torch.float64) 8 | import torch.nn as nn 9 | 10 | class Schrodinger1D(nn.Module): 11 | def __init__(self, xmesh): 12 | super(Schrodinger1D, self).__init__() 13 | 14 | self.xmesh = xmesh 15 | self.potential = nn.Parameter(xmesh**2) 16 | 17 | nmesh = xmesh.shape[0] 18 | h2 = (xmesh[1]-xmesh[0])**2 19 | self.K = torch.diag(1/h2*torch.ones(nmesh, dtype=xmesh.dtype), diagonal=0) \ 20 | - torch.diag(0.5/h2*torch.ones(nmesh-1, dtype=xmesh.dtype), diagonal=1) \ 21 | - torch.diag(0.5/h2*torch.ones(nmesh-1, dtype=xmesh.dtype), diagonal=-1) 22 | 23 | def _solve(self): 24 | 25 | H = torch.diag(self.potential) + self.K 26 | _, psi = torch.symeig(H, eigenvectors=True) 27 | 28 | return psi[:, 0] # 0 for ground state 29 | 30 | def forward(self, target): 31 | psi = self._solve() 32 | return (psi**2 - target).abs().sum() 33 | 34 | def plot(self, target): 35 | psi = self._solve() 36 | 37 | plt.cla() 38 | plt.plot(self.xmesh.numpy(), target.numpy(), label='target') 39 | plt.plot(self.xmesh.numpy(), psi.square().detach().numpy(), label='current') 40 | plt.plot(self.xmesh.numpy(), self.potential.detach().numpy()/10000, label='V/10000') 41 | plt.legend() 42 | plt.draw() 43 | 44 | if __name__=='__main__': 45 | #prepare mesh and target density 46 | xmin = -1; xmax = 1; Nmesh = 500 47 | xmesh = torch.linspace(xmin, xmax, Nmesh) 48 | 49 | target = torch.zeros(Nmesh) 50 | idx = torch.where(torch.abs(xmesh)<0.5) 51 | target[idx] = 1.-torch.abs(xmesh[idx]) 52 | target = (target/torch.norm(target))**2 53 | 54 | model = Schrodinger1D(xmesh) 55 | optimizer = torch.optim.LBFGS(model.parameters(), max_iter=10, tolerance_change = 1E-7, tolerance_grad=1E-7, line_search_fn='strong_wolfe') 56 | 57 | def closure(): 58 | optimizer.zero_grad() 59 | loss = model(target) # density difference 60 | loss.backward() 61 | return loss 62 | 63 | import matplotlib.pyplot as plt 64 | plt.ion() 65 | for epoch in range(50): 66 | loss = optimizer.step(closure) 67 | print (epoch, loss.item()) 68 | model.plot(target) 69 | plt.pause(0.01) 70 | plt.ioff() 71 | 72 | model.plot(target) 73 | plt.show() 74 | -------------------------------------------------------------------------------- /1_deep_learning/simple_ad.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | class NodeBase(object): 4 | 5 | def __init__(self, data): 6 | super(NodeBase, self).__init__() 7 | self._data = data 8 | 9 | def backward(self, delta): 10 | raise NotImplementedError 11 | 12 | @property 13 | def data(self): 14 | return self._data 15 | 16 | @data.setter 17 | def data(self, value): 18 | self._data = value 19 | 20 | def __repr__(self): 21 | return 'tracked ' + repr(self.data) 22 | 23 | def __radd__(self, rhs): 24 | return Node(Add, self, rhs) 25 | 26 | def __ladd__(self, lhs): 27 | return Node(Add, lhs, self) 28 | 29 | 30 | class Variable(NodeBase): 31 | 32 | def __init__(self, x, grad=None): 33 | super(Variable, self).__init__(x) 34 | self.grad = grad 35 | 36 | def backward(self, delta): 37 | if self.grad is None: 38 | self.grad = delta 39 | else: 40 | self.grad += delta 41 | return None 42 | 43 | 44 | class Node(NodeBase): 45 | 46 | def __init__(self, f, *args): 47 | data = [each.data if isinstance(each, NodeBase) else each for each in args] 48 | super(Node, self).__init__(f.eval(*data)) 49 | self.args = args 50 | self.args_data = data 51 | self.f = f 52 | 53 | def backward(self, delta): 54 | grads = self.f.gradient(delta, self.data, *self.args_data) 55 | for each, grad in zip(self.args, grads): 56 | if isinstance(each, NodeBase): 57 | each.backward(grad) 58 | return 59 | 60 | 61 | class Functional: 62 | 63 | def eval(self, *args): 64 | raise NotImplementedError 65 | 66 | def gradient(self, delta, output, *args): 67 | raise NotImplementedError 68 | 69 | 70 | class MatMul(Functional): 71 | 72 | @staticmethod 73 | def eval(A, B): 74 | return np.matmul(A, B) 75 | 76 | @staticmethod 77 | def gradient(delta, output, A, B): 78 | 79 | def adjoint(x): 80 | return np.conj(x.T) 81 | 82 | return np.matmul(delta, adjoint(B)), np.matmul(adjoint(A), delta) 83 | 84 | 85 | class Add(Functional): 86 | 87 | @staticmethod 88 | def eval(A, B): 89 | return A + B 90 | 91 | @staticmethod 92 | def gradient(delta, output, A, B): 93 | return delta, delta 94 | 95 | 96 | class Sigmoid(Functional): 97 | 98 | @staticmethod 99 | def eval(X): 100 | return 1. / (1. + np.exp(-X)) 101 | 102 | @staticmethod 103 | def gradient(delta, output, X): 104 | return delta * (1 - output) * output, 105 | 106 | 107 | def matmul(A, B): 108 | return Node(MatMul, A, B) 109 | 110 | def sigmoid(X): 111 | return Node(Sigmoid, X) 112 | 113 | 114 | if __name__ == '__main__': 115 | A = Variable(np.random.rand(2, 3)) 116 | B = Variable(np.random.rand(3, 4)) 117 | C = Variable(np.random.rand(2, 4)) 118 | 119 | # Dense 120 | Z = sigmoid(matmul(A, B) + C) 121 | Z.backward(np.random.rand(2, 4)) 122 | print(A.grad) 123 | -------------------------------------------------------------------------------- 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label='$%g$'%(i)) 17 | 18 | plt.legend() 19 | plt.show() 20 | -------------------------------------------------------------------------------- /2_tensor_network/README.md: -------------------------------------------------------------------------------- 1 | # Tensor Networks 2 | 3 | ## Table of Contents 4 | 5 | * [`Slides on tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/Tutorial_tensor_network.pdf) 6 | * [`Slides on contraction methods for infinite tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/tensor_contraction_methods.pdf) 7 | * [`Tutorial and demo codes on computing $2$-D Ising model partition function using tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/tensor_contraction_simple.ipynb) 8 | * [`Tutorial and demo codes on the MPS Born machine`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/mps_tutorial.ipynb) 9 | 10 | 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https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/2_tensor_network/imgs/two_qubits.png -------------------------------------------------------------------------------- /2_tensor_network/kacward.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | ''' 4 | Kac-Ward exact Ising 5 | See Theorem 1 of https://arxiv.org/abs/1011.3494 6 | ''' 7 | 8 | phi = np.array([[0., np.pi/2, -np.pi/2, np.nan ], 9 | [-np.pi/2, 0.0, np.nan, np.pi/2], 10 | [np.pi/2, np.nan, 0.0, -np.pi/2], 11 | [np.nan, -np.pi/2, np.pi/2, 0] 12 | ]) 13 | 14 | def logcosh(x): 15 | xp = np.abs(x) 16 | if (xp< 12): 17 | return np.log( np.cosh(x) ) 18 | else: 19 | return xp - np.log(2.) 20 | 21 | def neighborsite(i, n, L): 22 | """ 23 | The coordinate system is geometrically left->right, down -> up 24 | y| 25 | | 26 | | 27 | |________ x 28 | (0,0) 29 | So as a definition, l means x-1, r means x+1, u means y+1, and d means y-1 30 | """ 31 | x = i%L 32 | y = i//L # y denotes 33 | site = None 34 | # ludr : 35 | if (n==0): 36 | if (x-1>=0): 37 | site = (x-1) + y*L 38 | elif (n==1): 39 | if (y+1=0): 43 | site = x + (y-1)*L 44 | elif (n==3): 45 | if (x+1" 153 | ] 154 | }, 155 | "metadata": {}, 156 | "output_type": "display_data" 157 | } 158 | ], 159 | "source": [ 160 | "def show_imgs(imgs,l1=4,l2=5,s1=6,s2=6):\n", 161 | " \"\"\" Plot images \"\"\"\n", 162 | " plt.rcParams['figure.figsize']=(s1,s2)\n", 163 | " imgs=imgs.cpu().reshape([-1,28,28])\n", 164 | " g, ax = plt.subplots(l1,l2)\n", 165 | " for i in range(l1): \n", 166 | " for j in range(l2):\n", 167 | " a=i*l2+j\n", 168 | " if(a>=imgs.shape[0]):\n", 169 | " break\n", 170 | " ax[i][j].imshow(imgs[a,:,:],cmap='summer')\n", 171 | " ax[i][j].set_xticks([])\n", 172 | " ax[i][j].set_yticks([])\n", 173 | " plt.show()\n", 174 | " \n", 175 | "show_imgs(data,2,10,10,2)" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 23, 181 | "metadata": { 182 | "slideshow": { 183 | "slide_type": "subslide" 184 | } 185 | }, 186 | "outputs": [ 187 | { 188 | "name": "stdout", 189 | "output_type": "stream", 190 | "text": [ 191 | "shape of data is torch.Size([20, 784])\n", 192 | "tensor([[0, 0, 0, ..., 0, 0, 0],\n", 193 | " [0, 0, 0, ..., 0, 0, 0],\n", 194 | " [0, 0, 0, ..., 0, 0, 0],\n", 195 | " ...,\n", 196 | " [0, 0, 0, ..., 0, 0, 0],\n", 197 | " [0, 0, 0, ..., 0, 0, 0],\n", 198 | " [0, 0, 0, ..., 0, 0, 0]])\n" 199 | ] 200 | } 201 | ], 202 | "source": [ 203 | "print(\"shape of data is \",data.shape )\n", 204 | "print(data)" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": { 210 | "slideshow": { 211 | "slide_type": "slide" 212 | } 213 | }, 214 | "source": [ 215 | "### MPS initialization\n", 216 | "Define the mps, which is a list of 3-way tensors containing random values \n", 217 | "" 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": 20, 223 | "metadata": { 224 | "slideshow": { 225 | "slide_type": "fragment" 226 | } 227 | }, 228 | "outputs": [], 229 | "source": [ 230 | "Dmax=30 # maximum bond dimension\n", 231 | "#mydevice=torch.device(\"cuda:0\")\n", 232 | "mydevice=torch.device(\"cpu\")\n", 233 | "data=data.to(mydevice)\n", 234 | "bond_dims=[Dmax for i in range(n-1)]+[1]\n", 235 | "tensors= [ torch.randn(bond_dims[i-1],2,bond_dims[i],device=mydevice) for i in range(n)]\n", 236 | "\n" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": 24, 242 | "metadata": { 243 | "slideshow": { 244 | "slide_type": "subslide" 245 | } 246 | }, 247 | "outputs": [ 248 | { 249 | "name": "stdout", 250 | "output_type": "stream", 251 | "text": [ 252 | "shape of tensors is 784\n", 253 | "[torch.Size([1, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), 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torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 30]), torch.Size([30, 2, 1])]\n" 254 | ] 255 | } 256 | ], 257 | "source": [ 258 | "print(\"shape of tensors is\",len(tensors))\n", 259 | "print([i.shape for i in tensors])" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": { 265 | "slideshow": { 266 | "slide_type": "slide" 267 | } 268 | }, 269 | "source": [ 270 | "Now check the bond dimensions and tensors" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": 25, 276 | "metadata": { 277 | "scrolled": true, 278 | "slideshow": { 279 | "slide_type": "fragment" 280 | } 281 | }, 282 | "outputs": [ 283 | { 284 | "name": "stdout", 285 | "output_type": "stream", 286 | "text": [ 287 | "There are 784 tensors\n", 288 | "[30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1]\n" 289 | ] 290 | } 291 | ], 292 | "source": [ 293 | "print(\"There are\",len(tensors), \"tensors\")\n", 294 | "print(bond_dims)" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 26, 300 | "metadata": { 301 | "slideshow": { 302 | "slide_type": "subslide" 303 | } 304 | }, 305 | "outputs": [ 306 | { 307 | "name": "stdout", 308 | "output_type": "stream", 309 | "text": [ 310 | "shape of a tensor is torch.Size([30, 2, 30])\n" 311 | ] 312 | } 313 | ], 314 | "source": [ 315 | "print(\"shape of a tensor is\",tensors[5].shape)" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": { 321 | "slideshow": { 322 | "slide_type": "slide" 323 | } 324 | }, 325 | "source": [ 326 | "Question: does the contration with one image give a probability of the image?" 327 | ] 328 | }, 329 | { 330 | "cell_type": "markdown", 331 | "metadata": { 332 | "slideshow": { 333 | "slide_type": "fragment" 334 | } 335 | }, 336 | "source": [ 337 | "Answer: No" 338 | ] 339 | }, 340 | { 341 | "cell_type": "markdown", 342 | "metadata": { 343 | "slideshow": { 344 | "slide_type": "fragment" 345 | } 346 | }, 347 | "source": [ 348 | "\n", 349 | "" 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": { 355 | "slideshow": { 356 | "slide_type": "slide" 357 | } 358 | }, 359 | "source": [ 360 | "Canonicalization using QR decompositions\n", 361 | "* Left canonicalization using sequential QR decompositions. After that, all tensors except the right most ones are isometry with a column orthogonal unforded matrix\n", 362 | "\n" 363 | ] 364 | }, 365 | { 366 | "cell_type": "markdown", 367 | "metadata": { 368 | "slideshow": { 369 | "slide_type": "slide" 370 | } 371 | }, 372 | "source": [ 373 | "" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 28, 379 | "metadata": { 380 | "slideshow": { 381 | "slide_type": "subslide" 382 | } 383 | }, 384 | "outputs": [], 385 | "source": [ 386 | "def orthogonalize(site,going_right):\n", 387 | " dl=bond_dims[site-1] # left bond dimension\n", 388 | " d=bond_dims[site] # current bond dimension\n", 389 | " if(going_right):\n", 390 | " A=tensors[site].cpu().view(dl*2,d) # A is a matrix unfolded from the current tensor\n", 391 | " Q,R=torch.qr(A)\n", 392 | " R/=R.norm() # devided by norm \n", 393 | " tensors[site] = Q.contiguous().view(dl,2,-1).to(mydevice)\n", 394 | " tensors[site+1] = (R.to(mydevice)@tensors[site+1].view(d,-1)).view(-1,2,bond_dims[site+1])\n", 395 | " bond_dims[site] = Q.shape[1] # economy QR, so the right dimension could be either dl or d\n", 396 | " else: # going left\n", 397 | " A=tensors[site].cpu().view(dl,d*2).t()\n", 398 | " Q,R=torch.qr(A)\n", 399 | " R/=R.norm() \n", 400 | " tensors[site]=Q.t().contiguous().view(-1,2,d).to(mydevice)\n", 401 | " tensors[site-1] = (tensors[site-1].view(-1,dl)@R.t().to(mydevice)).view(bond_dims[site-2],2,-1)\n", 402 | " bond_dims[site-1] = Q.shape[1]\n" 403 | ] 404 | }, 405 | { 406 | "cell_type": "markdown", 407 | "metadata": { 408 | "slideshow": { 409 | "slide_type": "subslide" 410 | } 411 | }, 412 | "source": [ 413 | "After the canonicalization:\n", 414 | "* It make the partition function of the model equals to $1$. \n", 415 | "* The isometries have condition number $1$, preserving very well the computation precisions.\n", 416 | "\n", 417 | "" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": 30, 423 | "metadata": { 424 | "slideshow": { 425 | "slide_type": "subslide" 426 | } 427 | }, 428 | "outputs": [ 429 | { 430 | "name": "stdout", 431 | "output_type": "stream", 432 | "text": [ 433 | " site #783 / 784 time_used=0.67 Sec.\n" 434 | ] 435 | } 436 | ], 437 | "source": [ 438 | "t0=time.time()\n", 439 | "sys.stdout.write(\"Orthogonalizing...\\t\")\n", 440 | "for site in range(n-1):\n", 441 | " sys.stdout.write(\"\\r site #%d / %d \"%(site+1,n)); sys.stdout.flush()\n", 442 | " orthogonalize(site,True) \n", 443 | "sys.stdout.write(\"time_used=%.2f Sec.\\n\"%(time.time()-t0) ) " 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": 31, 449 | "metadata": { 450 | "slideshow": { 451 | "slide_type": "fragment" 452 | } 453 | }, 454 | "outputs": [ 455 | { 456 | "name": "stdout", 457 | "output_type": "stream", 458 | "text": [ 459 | "The shape of the last tensor is torch.Size([30, 2, 1])\n" 460 | ] 461 | } 462 | ], 463 | "source": [ 464 | "print(\"The shape of the last tensor is\",tensors[783].shape)" 465 | ] 466 | }, 467 | { 468 | "cell_type": "markdown", 469 | "metadata": { 470 | "slideshow": { 471 | "slide_type": "slide" 472 | } 473 | }, 474 | "source": [ 475 | "Canonicalization changes the bond dimensions from\n", 476 | "\n", 477 | "to\n", 478 | "" 479 | ] 480 | }, 481 | { 482 | "cell_type": "code", 483 | "execution_count": 33, 484 | "metadata": { 485 | "scrolled": true, 486 | "slideshow": { 487 | "slide_type": "subslide" 488 | } 489 | }, 490 | "outputs": [ 491 | { 492 | "name": "stdout", 493 | "output_type": "stream", 494 | "text": [ 495 | "[2, 4, 8, 16, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1]\n" 496 | ] 497 | } 498 | ], 499 | "source": [ 500 | "print(bond_dims)" 501 | ] 502 | }, 503 | { 504 | "cell_type": "markdown", 505 | "metadata": { 506 | "slideshow": { 507 | "slide_type": "slide" 508 | } 509 | }, 510 | "source": [ 511 | "Now contracting mps with one image gives the probability amplitude of the image, becuase\n", 512 | "\n", 513 | "and ?" 514 | ] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": 36, 519 | "metadata": { 520 | "slideshow": { 521 | "slide_type": "fragment" 522 | } 523 | }, 524 | "outputs": [], 525 | "source": [ 526 | "def get_psi():\n", 527 | " psi=torch.ones([m,1,1],device=mydevice)\n", 528 | " for site in range(n):\n", 529 | " psi = psi @ tensors[site][:,data[:,site],:].permute(1,0,2)\n", 530 | " return(psi)" 531 | ] 532 | }, 533 | { 534 | "cell_type": "markdown", 535 | "metadata": { 536 | "slideshow": { 537 | "slide_type": "slide" 538 | } 539 | }, 540 | "source": [ 541 | "Generating samples using the *ancestra* sampling\n", 542 | "" 543 | ] 544 | }, 545 | { 546 | "cell_type": "markdown", 547 | "metadata": { 548 | "slideshow": { 549 | "slide_type": "subslide" 550 | } 551 | }, 552 | "source": [ 553 | "" 554 | ] 555 | }, 556 | { 557 | "cell_type": "markdown", 558 | "metadata": { 559 | "slideshow": { 560 | "slide_type": "subslide" 561 | } 562 | }, 563 | "source": [ 564 | "" 565 | ] 566 | }, 567 | { 568 | "cell_type": "markdown", 569 | "metadata": { 570 | "slideshow": { 571 | "slide_type": "subslide" 572 | } 573 | }, 574 | "source": [ 575 | "" 576 | ] 577 | }, 578 | { 579 | "cell_type": "code", 580 | "execution_count": 38, 581 | "metadata": { 582 | "slideshow": { 583 | "slide_type": "fragment" 584 | } 585 | }, 586 | "outputs": [], 587 | "source": [ 588 | "def gen_samples(ns):\n", 589 | " samples=torch.zeros([ns,n],device=mydevice)\n", 590 | " for site in range(n-1):# left canonicalize\n", 591 | " orthogonalize(site,True) \n", 592 | " for s in range(ns):\n", 593 | " vec=torch.ones(1,1,device=mydevice)\n", 594 | " for site in range(n-1,-1,-1):\n", 595 | " vec = (tensors[site].view(-1,bond_dims[site])@vec).view(-1,2)\n", 596 | " p0 = vec[:,0].norm()**2/ (vec.norm()**2)\n", 597 | " x = (0 if np.random.rand() < p0 else 1)\n", 598 | " vec = vec[:,x]\n", 599 | " samples[s][site]=x\n", 600 | " return samples" 601 | ] 602 | }, 603 | { 604 | "cell_type": "markdown", 605 | "metadata": { 606 | "slideshow": { 607 | "slide_type": "slide" 608 | } 609 | }, 610 | "source": [ 611 | "### Initialize cache for MPS\n", 612 | "* Computing probability of a image --> contracting from the first tensor to the last tensor. " 613 | ] 614 | }, 615 | { 616 | "cell_type": "markdown", 617 | "metadata": { 618 | "slideshow": { 619 | "slide_type": "fragment" 620 | } 621 | }, 622 | "source": [ 623 | "* Lots of computation results can be re-used in the future computations." 624 | ] 625 | }, 626 | { 627 | "cell_type": "markdown", 628 | "metadata": { 629 | "slideshow": { 630 | "slide_type": "fragment" 631 | } 632 | }, 633 | "source": [ 634 | "* We would store the contraction results in the cache." 635 | ] 636 | }, 637 | { 638 | "cell_type": "markdown", 639 | "metadata": { 640 | "slideshow": { 641 | "slide_type": "fragment" 642 | } 643 | }, 644 | "source": [ 645 | "* Notice that the cache is for all images." 646 | ] 647 | }, 648 | { 649 | "cell_type": "code", 650 | "execution_count": 39, 651 | "metadata": { 652 | "slideshow": { 653 | "slide_type": "subslide" 654 | } 655 | }, 656 | "outputs": [ 657 | { 658 | "name": "stdout", 659 | "output_type": "stream", 660 | "text": [ 661 | "Caching...\ttime_used=0.13 Sec." 662 | ] 663 | } 664 | ], 665 | "source": [ 666 | "t0=time.time()\n", 667 | "cache=[] \n", 668 | "sys.stdout.write(\"Caching...\\t\")\n", 669 | "sys.stdout.flush()\n", 670 | "cache.append( torch.ones([m,1,1],device=mydevice)) # The initial elements, all images have cache 1\n", 671 | "for site in range(n-1):\n", 672 | " B=cache[site] @ tensors[site][:,data[:,site],:].permute(1,0,2)\n", 673 | " B /= B.abs().max()\n", 674 | " cache.append( B ) # batched matrix multiplications\n", 675 | "cache.append( torch.ones(m,1,1,device=mydevice)) # the last element, matrix [1,1] for all images\n", 676 | "sys.stdout.write(\"time_used=%.2f Sec.\"%(time.time()-t0) )" 677 | ] 678 | }, 679 | { 680 | "cell_type": "markdown", 681 | "metadata": { 682 | "slideshow": { 683 | "slide_type": "slide" 684 | } 685 | }, 686 | "source": [ 687 | "* ```B /= B.abs().max()``` is used to preserve computational precision" 688 | ] 689 | }, 690 | { 691 | "cell_type": "markdown", 692 | "metadata": { 693 | "slideshow": { 694 | "slide_type": "fragment" 695 | } 696 | }, 697 | "source": [ 698 | "* Because the gradient is invariant by multiplying the cache by a constant, for each image. " 699 | ] 700 | }, 701 | { 702 | "cell_type": "markdown", 703 | "metadata": { 704 | "slideshow": { 705 | "slide_type": "fragment" 706 | } 707 | }, 708 | "source": [ 709 | "* Length of the cache is $n+1$. " 710 | ] 711 | }, 712 | { 713 | "cell_type": "markdown", 714 | "metadata": { 715 | "slideshow": { 716 | "slide_type": "fragment" 717 | } 718 | }, 719 | "source": [ 720 | "* For an image, each pixel has a correponding vector, denoting the temporary results in tensor contractions (from left to right or from right to left)." 721 | ] 722 | }, 723 | { 724 | "cell_type": "markdown", 725 | "metadata": { 726 | "slideshow": { 727 | "slide_type": "subslide" 728 | } 729 | }, 730 | "source": [ 731 | "Let us look at the content of cache for image alpha=1:" 732 | ] 733 | }, 734 | { 735 | "cell_type": "code", 736 | "execution_count": 44, 737 | "metadata": { 738 | "scrolled": false, 739 | "slideshow": { 740 | "slide_type": "fragment" 741 | } 742 | }, 743 | "outputs": [ 744 | { 745 | "name": "stdout", 746 | "output_type": "stream", 747 | "text": [ 748 | "cache site 1 tensor([[1.]])\n", 749 | "cache site 2 tensor([[-0.0853, 1.0000]])\n", 750 | "cache site 3 tensor([[ 1.0000, 0.0972, -0.4895, 0.7524]])\n" 751 | ] 752 | } 753 | ], 754 | "source": [ 755 | "alpha=1 # the image 1\n", 756 | "print(\"cache site 1 \",cache[0][alpha])\n", 757 | "print(\"cache site 2 \",cache[1][alpha])\n", 758 | "print(\"cache site 3 \",cache[2][alpha])" 759 | ] 760 | }, 761 | { 762 | "cell_type": "markdown", 763 | "metadata": { 764 | "slideshow": { 765 | "slide_type": "subslide" 766 | } 767 | }, 768 | "source": [ 769 | "Then the probability amplitude $\\psi$ for images is given by $|\\psi|^2$ , let us check!" 770 | ] 771 | }, 772 | { 773 | "cell_type": "code", 774 | "execution_count": 42, 775 | "metadata": { 776 | "scrolled": true, 777 | "slideshow": { 778 | "slide_type": "fragment" 779 | } 780 | }, 781 | "outputs": [ 782 | { 783 | "name": "stdout", 784 | "output_type": "stream", 785 | "text": [ 786 | "Probability of generating image 3 = 0.00000\n" 787 | ] 788 | } 789 | ], 790 | "source": [ 791 | "psi=get_psi()\n", 792 | "print(\"Probability of generating image 3 = %.5f\"%(psi*psi)[3])" 793 | ] 794 | }, 795 | { 796 | "cell_type": "markdown", 797 | "metadata": { 798 | "slideshow": { 799 | "slide_type": "subslide" 800 | } 801 | }, 802 | "source": [ 803 | "**Let us plot probability of all training images**" 804 | ] 805 | }, 806 | { 807 | "cell_type": "code", 808 | "execution_count": 43, 809 | "metadata": { 810 | "slideshow": { 811 | "slide_type": "fragment" 812 | } 813 | }, 814 | "outputs": [ 815 | { 816 | "data": { 817 | "image/png": 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\n", 818 | "text/plain": [ 819 | "
" 820 | ] 821 | }, 822 | "metadata": { 823 | "needs_background": "light" 824 | }, 825 | "output_type": "display_data" 826 | } 827 | ], 828 | "source": [ 829 | "ax=plt.bar(range(1,psi.shape[0]+1),(psi.cpu()**2).squeeze())" 830 | ] 831 | }, 832 | { 833 | "cell_type": "markdown", 834 | "metadata": { 835 | "slideshow": { 836 | "slide_type": "subslide" 837 | } 838 | }, 839 | "source": [ 840 | "**Hey, the probability is $0$, what is wrong?** " 841 | ] 842 | }, 843 | { 844 | "cell_type": "markdown", 845 | "metadata": { 846 | "slideshow": { 847 | "slide_type": "fragment" 848 | } 849 | }, 850 | "source": [ 851 | "* Because the space is too large and the model is randomly initialized!" 852 | ] 853 | }, 854 | { 855 | "cell_type": "markdown", 856 | "metadata": { 857 | "slideshow": { 858 | "slide_type": "fragment" 859 | } 860 | }, 861 | "source": [ 862 | "* Indeed, the purpose of training is exactly to increase the probability of given images. " 863 | ] 864 | }, 865 | { 866 | "cell_type": "markdown", 867 | "metadata": { 868 | "slideshow": { 869 | "slide_type": "fragment" 870 | } 871 | }, 872 | "source": [ 873 | "* This is the so-called *maximum likelihood learning*." 874 | ] 875 | }, 876 | { 877 | "cell_type": "markdown", 878 | "metadata": { 879 | "slideshow": { 880 | "slide_type": "fragment" 881 | } 882 | }, 883 | "source": [ 884 | "* The procedure is sweeping back and force, from right to left, then from left to right. " 885 | ] 886 | }, 887 | { 888 | "cell_type": "markdown", 889 | "metadata": { 890 | "slideshow": { 891 | "slide_type": "fragment" 892 | } 893 | }, 894 | "source": [ 895 | "* During each sweep, the visited tensor is updated according to the *gradients* of the log-probability with respect to tensor elements." 896 | ] 897 | }, 898 | { 899 | "cell_type": "markdown", 900 | "metadata": { 901 | "slideshow": { 902 | "slide_type": "slide" 903 | } 904 | }, 905 | "source": [ 906 | "## Training MPS\n", 907 | "" 908 | ] 909 | }, 910 | { 911 | "cell_type": "markdown", 912 | "metadata": { 913 | "slideshow": { 914 | "slide_type": "slide" 915 | } 916 | }, 917 | "source": [ 918 | "** Computing gradient **\n", 919 | "\n", 920 | "$\\mathcal{L}=-\\frac{1}{m}\\sum_{\\mathbf{x}\\in\\mathrm{data}}\\ln P(\\mathbf{x})$\n", 921 | "\n", 922 | "$\\nabla \\mathcal{L}=-\\frac{2}{m}\\sum_{\\mathbf{x}\\in\\mathrm{data}}\\frac{\\psi'(\\mathbf{x})}{\\psi(\\mathbf{x})}+\\frac{Z'}{Z}$\n", 923 | "" 924 | ] 925 | }, 926 | { 927 | "cell_type": "markdown", 928 | "metadata": { 929 | "slideshow": { 930 | "slide_type": "slide" 931 | } 932 | }, 933 | "source": [ 934 | "Some tips on the code:\n", 935 | "* Only images with $i^{\\mathrm{th}}$ element $v_i$ contribute to tensor element $A_{w_{i-1},v_i,w_i}$.\n", 936 | "* ```torch.sum(left_vec.permute(0,2,1) @ right_vec.permute(0,2,1)``` computes $\\Psi'(\\mathbf{x})$.\n", 937 | "* ```psi = left_vec @ A.permute(1,0,2) @right_vec ``` does not compute _real_ $\\Psi$, as cache is rescaled to preserve precision. Thus one needs to call ```psi=get_psi()``` in order to compute correct probability amplitude $\\Psi$.\n" 938 | ] 939 | }, 940 | { 941 | "cell_type": "markdown", 942 | "metadata": { 943 | "slideshow": { 944 | "slide_type": "subslide" 945 | } 946 | }, 947 | "source": [ 948 | "* ```tensors_bak=tensors.copy()``` is to backup the training environment for restoring after generating images.\n", 949 | "* ```@``` operator in ```left_vec @ A.permute(1,0,2) @right_vec ``` and in ```cache[site] @ tensors[site][:,data[:,site],:].permute(1,0,2)``` works as batched matrix-matrix multiplications.\n", 950 | "* In ```orthogonalize()```, imposing matrix $R$ to be norm-one using ```R/=R.norm() ``` actually let the partition function $Z=1$." 951 | ] 952 | }, 953 | { 954 | "cell_type": "markdown", 955 | "metadata": {}, 956 | "source": [ 957 | "**Begin Learning:**" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": 45, 963 | "metadata": { 964 | "scrolled": false, 965 | "slideshow": { 966 | "slide_type": "slide" 967 | } 968 | }, 969 | "outputs": [ 970 | { 971 | "name": "stdout", 972 | "output_type": "stream", 973 | "text": [ 974 | " Epoch #0, site #783 / 784 NLL=72.145, LowerBound=2.996, total_prob=0.000 time_used=3.40 Sec.\n", 975 | " generating samples..." 976 | ] 977 | }, 978 | { 979 | "data": { 980 | "image/png": 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" 995 | ] 996 | }, 997 | "metadata": {}, 998 | "output_type": "display_data" 999 | }, 1000 | { 1001 | "name": "stdout", 1002 | "output_type": "stream", 1003 | "text": [ 1004 | "Press any key to continue, or 'stop' to quit stop\n" 1005 | ] 1006 | } 1007 | ], 1008 | "source": [ 1009 | "learning_rate=0.08\n", 1010 | "for epoch in range(9): # one sweep, from right to left, then from left to right\n", 1011 | " going_right=False\n", 1012 | " t0=time.time()\n", 1013 | " for site in [i for i in range(n-1,0,-1)]+[i for i in range(n-1)]:\n", 1014 | " # to update tensors[site] which is a 3-way tensor of size [dl,2,dr]\n", 1015 | " sys.stdout.write(\"\\r Epoch #%d, site #%d / %d \"%(epoch, site+1,n)); sys.stdout.flush()\n", 1016 | " if(site==0): going_right=True\n", 1017 | " gradients = torch.zeros_like(tensors[site],device=mydevice)\n", 1018 | " for i in [0,1]: # the pixel could be either 0 or 1\n", 1019 | " idx=(data[:,site]==i).nonzero().type(torch.LongTensor).squeeze() # this returns indices of non-zero elements\n", 1020 | " if(idx.numel()==0): continue\n", 1021 | " left_vec = cache[site][idx,:,:] # a vector on the left of the site\n", 1022 | " right_vec = cache[site+1][idx,:,:] # a vector on the right of the site\n", 1023 | " A=tensors[site][:,data[idx,site],:]\n", 1024 | " if(idx.numel()==1): \n", 1025 | " A=A.view(A.shape[0],1,A.shape[1])\n", 1026 | " left_vec=left_vec.view(1,left_vec.shape[0],left_vec.shape[1])\n", 1027 | " right_vec=right_vec.view(1,right_vec.shape[0],right_vec.shape[1])\n", 1028 | " psi = left_vec @ A.permute(1,0,2) @right_vec \n", 1029 | " gradients[:,i,:] = torch.sum(left_vec.permute(0,2,1) @ right_vec.permute(0,2,1) / psi,0) \n", 1030 | " gradients = 2.0*(gradients/m-tensors[site]) \n", 1031 | " tensors[site] += learning_rate * gradients/gradients.norm()\n", 1032 | " orthogonalize(site,going_right)\n", 1033 | " if(going_right): # Update cache\n", 1034 | " cache[site+1] = cache[site] @ tensors[site][:,data[:,site],:].permute(1,0,2)\n", 1035 | " cache[site+1] /= cache[site+1].abs().max()\n", 1036 | " else:\n", 1037 | " cache[site] = tensors[site][:,data[:,site],:].permute(1,0,2) @ cache[site+1]\n", 1038 | " cache[site] /= cache[site].abs().max()\n", 1039 | " psi=get_psi()\n", 1040 | " tensors_bak=tensors.copy()\n", 1041 | " sys.stdout.write(\"NLL=%.3f, LowerBound=%.3f, total_prob=%.3f time_used=%.2f Sec.\"%(-torch.mean(torch.log(psi*psi)),math.log(m),torch.sum(psi.squeeze()**2),time.time()-t0 ))\n", 1042 | " sys.stdout.write(\"\\n generating samples...\")\n", 1043 | " ax=plt.bar(range(1,psi.shape[0]+1),psi.cpu().squeeze()**2)\n", 1044 | " imgs=gen_samples(30)\n", 1045 | " show_imgs(imgs,2,15,15,2)\n", 1046 | " tensors=tensors_bak.copy()\n", 1047 | " if(epoch < 8):\n", 1048 | " a=input(\"Press any key to continue, or 'stop' to quit \")\n", 1049 | " if (a==\"stop\"):\n", 1050 | " break\n" 1051 | ] 1052 | }, 1053 | { 1054 | "cell_type": "markdown", 1055 | "metadata": {}, 1056 | "source": [ 1057 | "### References for further reading:\n", 1058 | "* Ulrich Schollwock, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics 326, 96– 192 (2011).\n", 1059 | "* Tamara G. Kolda, and Brett W. Bader, \"Tensor decompositions and applications\", SIAM review 51, 455 (2009).\n", 1060 | "* Ivan V Oseledets, “Tensor-train decomposition,” SIAM Journal on Scientific Computing 33, 2295–2317 (2011).\n", 1061 | "* Edwin Miles Miles Stoudenmire and David J. Schwab, “Supervised Learning with Quantum-Inspired Tensor Networks,” Advances in Neural Information Processing Systems 29, 4799 (2016), arXiv:1605.05775.\n", 1062 | "* Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets, “Exponential machines,” arXiv:1605.05775 (2016).\n", 1063 | "* Jinguo Liu, and Lei Wang,Differentiable Learning of Quantum Circuit Born Machine, arXiv:1804.04168 (2018).\n", 1064 | "* Edwin Miles Stoudenmire, “Learning relevant features of data with multi-scale tensor networks,” Quantum Science and Technology (2018)." 1065 | ] 1066 | }, 1067 | { 1068 | "cell_type": "code", 1069 | "execution_count": null, 1070 | "metadata": {}, 1071 | "outputs": [], 1072 | "source": [] 1073 | } 1074 | ], 1075 | "metadata": { 1076 | "celltoolbar": "Slideshow", 1077 | "kernelspec": { 1078 | "display_name": "Python 3", 1079 | "language": "python", 1080 | "name": "python3" 1081 | }, 1082 | "language_info": { 1083 | "codemirror_mode": { 1084 | "name": "ipython", 1085 | "version": 3 1086 | }, 1087 | "file_extension": ".py", 1088 | "mimetype": "text/x-python", 1089 | "name": "python", 1090 | "nbconvert_exporter": "python", 1091 | "pygments_lexer": "ipython3", 1092 | "version": "3.7.3" 1093 | } 1094 | }, 1095 | "nbformat": 4, 1096 | "nbformat_minor": 2 1097 | } 1098 | -------------------------------------------------------------------------------- /2_tensor_network/tensor_contraction_methods.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/2_tensor_network/tensor_contraction_methods.pdf -------------------------------------------------------------------------------- /4_quantum/README.md: -------------------------------------------------------------------------------- 1 | # Quantum Computing 2 | 3 | ## Table of Contents 4 | * Lecture Note: [quantum_lecture_note.pdf](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/quantum_lecture_note.pdf) 5 | * Slides: [google slides](https://docs.google.com/presentation/d/1jUTpa8pB3jEOWDW1U0rDTDQ-kpri8j8S4y77GQCo3iM/edit?usp=sharing) 6 | * Notebooks 7 | * The solution to the graph embeding problem: [graph_embeding.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/graph_embeding.ipynb) 8 | * Quantum circuit computing with Yao.jl: [QC-with-Yao.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/QC-with-Yao.ipynb) 9 | * Landscape of a quantum circuit: [variational_quantum_circuit.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) 10 | * Variational quantum eigensolver: [variational_quantum_circuit.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) 11 | * Matrix Product state inspired variational quantum eigensolver [VQE_action.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/VQE_action.ipynb) 12 | * Quantum circuit born machine: [qcbm_gaussian.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/qcbm_gaussian.ipynb) 13 | * Gradient vanishing problem: [variational_quantum_circuit.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) and [VQE_action.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/VQE_action.ipynb) 14 | * Mapping a quantum circuit to tensor networks: [qc_tensor_mapping.ipynb](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/qc_tensor_mapping.ipynb) 15 | 16 | Welcome for pull requests and issues! 17 | -------------------------------------------------------------------------------- /4_quantum/bloch_sphere.jl: -------------------------------------------------------------------------------- 1 | using Makie, GLMakie, Observables, Colors, Yao 2 | AbstractPlotting.inline!(true) 3 | 4 | function bloch_arrow(θ, ϕ) 5 | x = sin(2θ) * cos(ϕ) 6 | y = sin(2θ) * sin(ϕ) 7 | z = cos(2θ) 8 | return (x, y, z) 9 | end 10 | 11 | function bloch_arrow(r::ArrayReg) 12 | @assert nqubits(r) == 1 "invalid quantum state, expect only one qubit" 13 | st = statevec(r) 14 | global_phase = exp(im * angle(st[1])) 15 | st = st ./ global_phase 16 | θ = acos(real(st[1])) # st[1] is real 17 | ϕ = iszero(θ) ? zero(θ) : angle(st[2] / sin(θ)) 18 | return bloch_arrow(θ, ϕ) 19 | end 20 | 21 | function plot_arrow!(scene, r::ArrayReg; kwargs...) 22 | x, y, z = bloch_arrow(r) 23 | lines!(scene, [0.0, x], [0.0, y], [0.0, z]; kwargs...) 24 | return scene 25 | end 26 | 27 | function plot_block!(scene, x::AbstractBlock; linewidth=5.0, kwargs...) 28 | r = ArrayReg(bit"0") 29 | plot_arrow!(scene, r; color=:red, linewidth=linewidth, kwargs...) 30 | rand_color = RGB(rand(0:255)/255, rand(0:255)/255, rand(0:255)/255) 31 | plot_arrow!(scene, apply!(r, x); color=rand_color ,linewidth=linewidth, kwargs...) 32 | return scene 33 | end 34 | 35 | function bloch_sphere!(scene) 36 | wireframe!(scene, Sphere(Point3f0(0), 1f0), color=:cyan3, linewidth=0.1, thickness = 0.6f0, transparency = true) 37 | return scene 38 | end 39 | 40 | 41 | function plotapply(x::AbstractBlock) 42 | AbstractPlotting.inline!(false) 43 | scene = Scene(resolution=(1000, 1000), center=false) 44 | bloch_sphere!(scene) 45 | plot_block!(scene, x) 46 | rotate_cam!(scene, 0.5, 0.0, 0.0) 47 | return scene 48 | end 49 | 50 | function animate_rot() 51 | scene = Scene(resolution=(1000, 1000)) 52 | bloch_sphere!(scene) 53 | scene.center = false 54 | 55 | time = Node(0.0) 56 | scene = lift() 57 | bloch_arrow!(scene, x) 58 | rotate_cam!(scene, 0.5, 0.0, 0.0) 59 | end 60 | 61 | # Pauli Gates 62 | function animate_rot(blk::AbstractBlock) 63 | AbstractPlotting.inline!(false) 64 | screen = GLMakie.Screen() 65 | GLMakie.GLFW.set_visibility!(GLMakie.to_native(screen), AbstractPlotting.use_display[]) 66 | 67 | AbstractPlotting.inline!(true) 68 | scene = Scene(resolution=(1000, 1000), center=false) 69 | 70 | 71 | bloch_sphere!(scene) 72 | 73 | r = ArrayReg(bit"0") 74 | plot_arrow!(scene, r; color=:red, linewidth=5.0) 75 | time = Node(0.0) 76 | 77 | lifted = lift(time) do t 78 | x, y, z = bloch_arrow(apply!(r, rot(blk, t))) 79 | [0.0, x], [0.0, y], [0.0, z] 80 | end 81 | 82 | lines!(scene, lifted; color=:blue, linewidth=5.0) 83 | rotate_cam!(scene, 0.1, 0.0, 0.0) 84 | 85 | for _ in 1:100 86 | sleep(0.1) 87 | push!(time, to_value(time) + 0.001) 88 | display(screen, scene) 89 | end 90 | return scene 91 | end 92 | 93 | # 1. plot pauli gates 94 | plotapply(X) 95 | 96 | # 2. plot rotate animation 97 | scene = animate_rot(chain(X, H, X)) 98 | 99 | # 3. make video 100 | scene = Scene(resolution=(1000, 1000), center=false) 101 | bloch_sphere!(scene) 102 | r = ArrayReg(bit"0") 103 | plot_arrow!(scene, r; color=:red, linewidth=5.0) 104 | 105 | T = Node(0.0) 106 | lifted = lift(T) do t 107 | x, y, z = bloch_arrow(apply!(r, rot(X, t))) 108 | [0.0, x], [0.0, y], [0.0, z] 109 | end 110 | 111 | lines!(scene, lifted; color=:blue, linewidth=5.0) 112 | rotate_cam!(scene, 0.1, 0.0, 0.0) 113 | 114 | record(scene, "bloch.gif", 1:100) do i 115 | push!(T, 0.001 * i) 116 | end 117 | -------------------------------------------------------------------------------- /4_quantum/graph_embeding.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "scrolled": true 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stderr", 12 | "output_type": "stream", 13 | "text": [ 14 | "┌ Info: Recompiling stale cache file /home/leo/.julia/compiled/v1.1/Zygote/4kbLI.ji for Zygote [e88e6eb3-aa80-5325-afca-941959d7151f]\n", 15 | "└ @ Base loading.jl:1184\n", 16 | "┌ Info: Recompiling stale cache file /home/leo/.julia/compiled/v1.1/Flux/QdkVy.ji for Flux [587475ba-b771-5e3f-ad9e-33799f191a9c]\n", 17 | "└ @ Base loading.jl:1184\n" 18 | ] 19 | } 20 | ], 21 | "source": [ 22 | "using Zygote\n", 23 | "using Statistics: var, mean\n", 24 | "using LinearAlgebra: norm\n", 25 | "using Flux.NNlib: relu\n", 26 | "using Flux.Optimise" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 2, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/plain": [ 37 | "myvar (generic function with 1 method)" 38 | ] 39 | }, 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "output_type": "execute_result" 43 | } 44 | ], 45 | "source": [ 46 | "function myvar(v)\n", 47 | " mv = mean(v)\n", 48 | " sum((v .- mv).^2)./(length(v)-1)\n", 49 | "end" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 3, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "data": { 59 | "text/plain": [ 60 | "train (generic function with 1 method)" 61 | ] 62 | }, 63 | "execution_count": 3, 64 | "metadata": {}, 65 | "output_type": "execute_result" 66 | } 67 | ], 68 | "source": [ 69 | "function train(params)\n", 70 | " opt = ADAM(0.01)\n", 71 | " V = 8\n", 72 | " maxiter = 20000\n", 73 | " msk = [false, true, true, true, false, true, true, true]\n", 74 | " pp = params[:,msk]\n", 75 | " for i=1:maxiter\n", 76 | " grad = view(loss'(params), :,msk)\n", 77 | " Optimise.update!(opt, pp, grad)\n", 78 | " view(params, :, msk) .= pp\n", 79 | " if i%100 == 0\n", 80 | " @show loss(params)\n", 81 | " end\n", 82 | " end\n", 83 | " params\n", 84 | "end" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 4, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "15-element Array{Tuple{Int64,Int64},1}:\n", 96 | " (1, 6) \n", 97 | " (2, 7) \n", 98 | " (3, 8) \n", 99 | " (4, 9) \n", 100 | " (5, 10)\n", 101 | " (1, 2) \n", 102 | " (2, 3) \n", 103 | " (3, 4) \n", 104 | " (4, 5) \n", 105 | " (1, 5) \n", 106 | " (6, 8) \n", 107 | " (8, 10)\n", 108 | " (7, 10)\n", 109 | " (7, 9) \n", 110 | " (6, 9) " 111 | ] 112 | }, 113 | "execution_count": 4, 114 | "metadata": {}, 115 | "output_type": "execute_result" 116 | } 117 | ], 118 | "source": [ 119 | "L1 = [(1,6), (2,7), (3,8), (4,9), (5,10), (1,2), (2,3), (3,4), (4,5), (5,1), (6,8), (8,10), (10,7), (7,9), (9,6)]\n", 120 | "L1 = [i 4 | 5 | ## 赛题 6 | 在如图所示的Buckyball结构中，在每一个顶点上有一个 的伊辛自旋。近邻的自旋之间有单位强度的反铁磁耦合。 7 | 8 | 1. 求，其中 是顶点数，而 9 | 10 | 11 | 12 | 2. 求基态简并度。 13 | 14 | ----------------------------------------- 15 | 16 | *translation*: In the Buckyball structure as shown in the figure, we attach an ising spin on each vertex. The neighboring spins interact with an anti-ferromagnetic coupling of unit strength. 17 | 18 | 1. Get , where is the number of vertices, and 19 | 20 | 21 | 22 | 2. Count the ground state degeneracy. 23 | 24 | ## 比赛规则 25 | - 个人或组队参加皆可。组队参加时，奖品分配由内部协商决定。 26 | - 比赛截止时间北京时间5月10日0时。以此时刻之前commit到GiHub的求解程序和结果为评判标准。 27 | - 编程语言不限，但商业软件需有正版授权。编译，JIT 和软件启动不计入运行时间。 28 | - 第一问是必答题，第二问是附加题。 29 | - 鼓励探索多种解法。结果数值精度、程序效率和原创性都是评分的重要标准。优胜队由授课教师投票评选。唯一的优胜队获得奖品。 30 | - 求解程序须采用开源协议。代码版权归参赛者所有。 31 | - 对参赛有实质贡献的合作者须在程序中明确列出。 32 | 33 | ## 比赛流程 34 | 35 | - 在GitHub上建立私有代码库，添加用户`quantumbfs0` 作为合作者 (Settings/Collaborators)。 36 | - 开发求解程序 :soccer:。 37 | - 代码库中除了包含求解程序之外，在文件`ans1.txt`和`ans2.txt`中以文本格式分别存储两问的答案。 38 | - 周三下午是自由编程时间，授课教师在会场为大家解决技术问题。 39 | - 5月10日0时以后公开代码库供评审和学习。 40 | - 优胜队、或多个取得正确结果的赛队参加周五上午的答辩，分享求解思路和程序实现。 41 | 42 | ## [Submissions](https://github.com/quantumbfs0?tab=stars) 43 | 44 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 QuantumBFS 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Manifest.toml: -------------------------------------------------------------------------------- 1 | # This file is machine-generated - editing it directly is not advised 2 | 3 | [[AbstractFFTs]] 4 | deps = ["LinearAlgebra", "Test"] 5 | git-tree-sha1 = "dfaf23dba016254bb0eed6de326510caea2889bf" 6 | uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" 7 | version = "0.4.0" 8 | 9 | [[AbstractTrees]] 10 | deps = ["Markdown", "Test"] 11 | git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b" 12 | uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" 13 | version = "0.2.1" 14 | 15 | [[Adapt]] 16 | deps = ["LinearAlgebra", "Test"] 17 | 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"bbf7d656-a473-5ed7-a52c-81e309532950" 100 | version = "0.2.0" 101 | 102 | [[Compat]] 103 | deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"] 104 | git-tree-sha1 = "84aa74986c5b9b898b0d1acaf3258741ee64754f" 105 | uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" 106 | version = "2.1.0" 107 | 108 | [[Conda]] 109 | deps = ["Compat", "JSON", "VersionParsing"] 110 | git-tree-sha1 = "b625d802587c2150c279a40a646fba63f9bd8187" 111 | uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d" 112 | version = "1.2.0" 113 | 114 | [[Contour]] 115 | deps = ["LinearAlgebra", "StaticArrays", "Test"] 116 | git-tree-sha1 = "b974e164358fea753ef853ce7bad97afec15bb80" 117 | uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" 118 | version = "0.5.1" 119 | 120 | [[Crayons]] 121 | deps = ["Test"] 122 | git-tree-sha1 = "f621b8ef51fd2004c7cf157ea47f027fdeac5523" 123 | uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" 124 | version = "4.0.0" 125 | 126 | [[DataStructures]] 127 | deps = ["InteractiveUtils", "OrderedCollections", "Random", "Serialization", "Test"] 128 | git-tree-sha1 = "ca971f03e146cf144a9e2f2ce59674f5bf0e8038" 129 | uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" 130 | version = "0.15.0" 131 | 132 | [[Dates]] 133 | deps = ["Printf"] 134 | uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" 135 | 136 | [[DelimitedFiles]] 137 | deps = ["Mmap"] 138 | uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" 139 | 140 | [[DiffEqBase]] 141 | deps = ["Compat", "DocStringExtensions", "IterativeSolvers", "IteratorInterfaceExtensions", "LinearAlgebra", "RecipesBase", "RecursiveArrayTools", "RecursiveFactorization", "Requires", "Roots", "SparseArrays", "StaticArrays", "Statistics", "SuiteSparse", "TableTraits", "TreeViews"] 142 | git-tree-sha1 = "c4560b4ae808770bc58a31f8c796b3f195e6239a" 143 | uuid = "2b5f629d-d688-5b77-993f-72d75c75574e" 144 | version = "5.7.0" 145 | 146 | [[DiffResults]] 147 | deps = ["Compat", "StaticArrays"] 148 | git-tree-sha1 = "34a4a1e8be7bc99bc9c611b895b5baf37a80584c" 149 | uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5" 150 | version = "0.0.4" 151 | 152 | [[DiffRules]] 153 | deps = ["Random", "Test"] 154 | git-tree-sha1 = "dc0869fb2f5b23466b32ea799bd82c76480167f7" 155 | uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" 156 | version = "0.0.10" 157 | 158 | [[Distributed]] 159 | deps = ["Random", "Serialization", "Sockets"] 160 | uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" 161 | 162 | [[DocStringExtensions]] 163 | deps = ["LibGit2", "Markdown", "Pkg", "Test"] 164 | git-tree-sha1 = "4d30e889c9f106a51ffa4791a88ffd4765bf20c3" 165 | uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" 166 | version = "0.7.0" 167 | 168 | [[ExponentialUtilities]] 169 | deps = ["LinearAlgebra", "Printf", "Random", "SparseArrays", "Test"] 170 | git-tree-sha1 = "6fad21cd7637d0ad6a7661f8abea1149922d6c9c" 171 | uuid = "d4d017d3-3776-5f7e-afef-a10c40355c18" 172 | version = "1.4.0" 173 | 174 | [[FFTW]] 175 | deps = ["AbstractFFTs", "BinaryProvider", "Compat", "Conda", "Libdl", "LinearAlgebra", "Reexport", "Test"] 176 | git-tree-sha1 = "29cda58afbf62f35b1a094882ad6c745a47b2eaa" 177 | uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" 178 | version = "0.2.4" 179 | 180 | [[FileWatching]] 181 | uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" 182 | 183 | [[FixedPointNumbers]] 184 | deps = ["Test"] 185 | git-tree-sha1 = "b8045033701c3b10bf2324d7203404be7aef88ba" 186 | uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" 187 | version = "0.5.3" 188 | 189 | [[Flux]] 190 | deps = ["AbstractTrees", "Adapt", "CodecZlib", "Colors", "DelimitedFiles", "Juno", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SHA", "Statistics", "StatsBase", "Tracker", "ZipFile"] 191 | git-tree-sha1 = "e0740e96abfc84c305a1d3f3bb7cb4eade842027" 192 | repo-rev = "master" 193 | repo-url = "https://github.com/FluxML/Flux.jl.git" 194 | uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c" 195 | version = "0.8.3" 196 | 197 | [[ForwardDiff]] 198 | deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "InteractiveUtils", "LinearAlgebra", "NaNMath", "Random", "SparseArrays", "SpecialFunctions", "StaticArrays", "Test"] 199 | git-tree-sha1 = "4c4d727f1b7e0092134fabfab6396b8945c1ea5b" 200 | uuid = "f6369f11-7733-5829-9624-2563aa707210" 201 | version = "0.10.3" 202 | 203 | [[FunctionalCollections]] 204 | deps = ["Test"] 205 | git-tree-sha1 = "04cb9cfaa6ba5311973994fe3496ddec19b6292a" 206 | uuid = "de31a74c-ac4f-5751-b3fd-e18cd04993ca" 207 | version = "0.5.0" 208 | 209 | [[GR]] 210 | deps = ["Base64", "DelimitedFiles", "LinearAlgebra", "Pkg", "Printf", "Random", "Serialization", "Sockets", "Test"] 211 | git-tree-sha1 = "537b61986d8a5fd21e577541f8f0735e481eb3ed" 212 | uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" 213 | version = "0.39.1" 214 | 215 | [[IJulia]] 216 | deps = ["Base64", "Conda", "Dates", "InteractiveUtils", "JSON", "Markdown", "MbedTLS", "Pkg", "Printf", "REPL", "Random", "SoftGlobalScope", "Test", "UUIDs", "ZMQ"] 217 | git-tree-sha1 = "8eb8459d806de665f1347b25e9ad9428c2609f0f" 218 | uuid = "7073ff75-c697-5162-941a-fcdaad2a7d2a" 219 | version = "1.18.1" 220 | 221 | [[IRTools]] 222 | deps = ["InteractiveUtils", "MacroTools", "Test"] 223 | git-tree-sha1 = "1a946e462d8a86cc60df2cbfd4ff9c2ed9ee2615" 224 | repo-rev = "master" 225 | repo-url = "https://github.com/MikeInnes/IRTools.jl.git" 226 | uuid = "7869d1d1-7146-5819-86e3-90919afe41df" 227 | version = "0.2.0" 228 | 229 | [[Interact]] 230 | deps = ["CSSUtil", "InteractBase", "JSON", "Knockout", "Observables", "OrderedCollections", "Reexport", "Test", "WebIO", "Widgets"] 231 | git-tree-sha1 = "f6531bc554990a6659966afcb8f05543c032068b" 232 | uuid = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1" 233 | version = "0.10.2" 234 | 235 | [[InteractBase]] 236 | deps = ["Base64", "CSSUtil", "Colors", "Dates", "JSExpr", "JSON", "Knockout", "Observables", "OrderedCollections", "Random", "Test", "WebIO", "Widgets"] 237 | git-tree-sha1 = "83fc45f21bfec97e5cd56e790f6945497ab0f095" 238 | uuid = "d3863d7c-f0c8-5437-a7b4-3ae773c01009" 239 | version = "0.10.1" 240 | 241 | [[InteractiveUtils]] 242 | deps = ["Markdown"] 243 | uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" 244 | 245 | [[IterativeSolvers]] 246 | deps = ["LinearAlgebra", "Printf", "Random", "RecipesBase", "SparseArrays", "Test"] 247 | git-tree-sha1 = "5687f68018b4f14c0da54d402bb23eecaec17f37" 248 | uuid = "42fd0dbc-a981-5370-80f2-aaf504508153" 249 | version = "0.8.1" 250 | 251 | [[IteratorInterfaceExtensions]] 252 | git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" 253 | uuid = "82899510-4779-5014-852e-03e436cf321d" 254 | version = "1.0.0" 255 | 256 | [[JSExpr]] 257 | deps = ["JSON", "MacroTools", "Observables", "Test", "WebIO"] 258 | git-tree-sha1 = "013bc2143a2e84ea489365cf30db3407deb540c2" 259 | uuid = "97c1335a-c9c5-57fe-bc5d-ec35cebe8660" 260 | version = "0.5.0" 261 | 262 | [[JSON]] 263 | deps = ["Dates", "Distributed", "Mmap", "Sockets", "Test", "Unicode"] 264 | git-tree-sha1 = "1f7a25b53ec67f5e9422f1f551ee216503f4a0fa" 265 | uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" 266 | version = "0.20.0" 267 | 268 | [[JuliaInterpreter]] 269 | deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"] 270 | git-tree-sha1 = "553281260578bb50d6d550977f438754e7e93397" 271 | uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a" 272 | version = "0.5.1" 273 | 274 | [[Juno]] 275 | deps = ["Base64", "Logging", "Media", "Profile", "Test"] 276 | git-tree-sha1 = "4e4a8d43aa7ecec66cadaf311fbd1e5c9d7b9175" 277 | uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d" 278 | version = "0.7.0" 279 | 280 | [[Knockout]] 281 | deps = ["JSExpr", "JSON", "Observables", "Pkg", "Test", "WebIO"] 282 | git-tree-sha1 = "5cca7f070f85392cd0f42220c8ea5f7be96dacf4" 283 | uuid = "bcebb21b-c2e3-54f8-a781-646b90f6d2cc" 284 | version = "0.2.2" 285 | 286 | [[KrylovKit]] 287 | deps = ["LinearAlgebra", "Printf", "Random", "Test"] 288 | git-tree-sha1 = "b20b380cc5e28f079ba5212450d7d42a10a85194" 289 | uuid = "0b1a1467-8014-51b9-945f-bf0ae24f4b77" 290 | version = "0.3.4" 291 | 292 | [[LaTeXStrings]] 293 | deps = ["Compat"] 294 | git-tree-sha1 = "7ab9b8788cfab2bdde22adf9004bda7ad9954b6c" 295 | uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" 296 | version = "1.0.3" 297 | 298 | [[Latexify]] 299 | deps = ["InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "Printf", "Requires", "Test"] 300 | git-tree-sha1 = "5ea2cc735d06ef3abbac12dcc8403cf1f4999bf7" 301 | uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" 302 | version = "0.8.2" 303 | 304 | [[LegibleLambdas]] 305 | deps = ["MLStyle", "MacroTools", "Test"] 306 | git-tree-sha1 = "987298a681f4946b0d5d87ded1e89b53957d3b07" 307 | uuid = "f1f30506-32fe-5131-bd72-7c197988f9e5" 308 | version = "0.2.0" 309 | 310 | [[LibGit2]] 311 | uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" 312 | 313 | [[Libdl]] 314 | uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" 315 | 316 | [[LinearAlgebra]] 317 | deps = ["Libdl"] 318 | uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" 319 | 320 | [[LinearMaps]] 321 | deps = ["LinearAlgebra", "SparseArrays", "Test"] 322 | git-tree-sha1 = "ea9bb3d793917bced9c98da5ed8b6f449c4a74ba" 323 | uuid = "7a12625a-238d-50fd-b39a-03d52299707e" 324 | version = "2.3.0" 325 | 326 | [[Logging]] 327 | uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" 328 | 329 | [[LoweredCodeUtils]] 330 | deps = ["JuliaInterpreter"] 331 | git-tree-sha1 = "c530b90b64249b525f1e40e71f64d5382c62f073" 332 | uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b" 333 | version = "0.3.4" 334 | 335 | [[LuxurySparse]] 336 | deps = ["LinearAlgebra", "Random", "SparseArrays", "StaticArrays"] 337 | git-tree-sha1 = "003ae3a72b221a5fbe08e59965288268b4661efb" 338 | uuid = "d05aeea4-b7d4-55ac-b691-9e7fabb07ba2" 339 | version = "0.4.0" 340 | 341 | [[MLStyle]] 342 | deps = ["Statistics", "Test"] 343 | git-tree-sha1 = "12d2f421dcff9c12b2aebcc25759f0cdca16a16b" 344 | uuid = "d8e11817-5142-5d16-987a-aa16d5891078" 345 | version = "0.3.0" 346 | 347 | [[MacroTools]] 348 | deps = ["CSTParser", "Compat", "DataStructures", "Test"] 349 | git-tree-sha1 = "daecd9e452f38297c686eba90dba2a6d5da52162" 350 | uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" 351 | version = "0.5.0" 352 | 353 | [[Markdown]] 354 | deps = ["Base64"] 355 | uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" 356 | 357 | [[MbedTLS]] 358 | deps = ["BinaryProvider", "Dates", "Distributed", "Libdl", "Random", "Sockets", "Test"] 359 | git-tree-sha1 = "2d94286a9c2f52c63a16146bb86fd6cdfbf677c6" 360 | uuid = "739be429-bea8-5141-9913-cc70e7f3736d" 361 | version = "0.6.8" 362 | 363 | [[Measures]] 364 | deps = ["Test"] 365 | git-tree-sha1 = "ddfd6d13e330beacdde2c80de27c1c671945e7d9" 366 | uuid = "442fdcdd-2543-5da2-b0f3-8c86c306513e" 367 | version = "0.3.0" 368 | 369 | [[Media]] 370 | deps = ["MacroTools", "Test"] 371 | git-tree-sha1 = "75a54abd10709c01f1b86b84ec225d26e840ed58" 372 | uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27" 373 | version = "0.5.0" 374 | 375 | [[Missings]] 376 | deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"] 377 | git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042" 378 | uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" 379 | version = "0.4.0" 380 | 381 | [[Mmap]] 382 | uuid = "a63ad114-7e13-5084-954f-fe012c677804" 383 | 384 | [[NNlib]] 385 | deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics", "TimerOutputs"] 386 | git-tree-sha1 = "b7d9624a6c67b6cef981322692e2d0b35a010c63" 387 | repo-rev = "master" 388 | repo-url = "https://github.com/FluxML/NNlib.jl.git" 389 | uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" 390 | version = "0.6.0" 391 | 392 | [[NaNMath]] 393 | deps = ["Compat"] 394 | git-tree-sha1 = "ce3b85e484a5d4c71dd5316215069311135fa9f2" 395 | uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3" 396 | version = "0.3.2" 397 | 398 | [[Observables]] 399 | deps = ["Test"] 400 | git-tree-sha1 = "dc02cec22747d1d10d9f70d8a1c03432b5bfbcd0" 401 | uuid = "510215fc-4207-5dde-b226-833fc4488ee2" 402 | version = "0.2.3" 403 | 404 | [[OrderedCollections]] 405 | deps = ["Random", "Serialization", "Test"] 406 | git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1" 407 | uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" 408 | version = "1.1.0" 409 | 410 | [[Pidfile]] 411 | deps = ["FileWatching", "Test"] 412 | git-tree-sha1 = "1ffd82728498b5071cde851bbb7abd780d4445f3" 413 | uuid = "fa939f87-e72e-5be4-a000-7fc836dbe307" 414 | version = "1.1.0" 415 | 416 | [[Pkg]] 417 | deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"] 418 | uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" 419 | 420 | [[PlotThemes]] 421 | deps = ["PlotUtils", "Requires", "Test"] 422 | git-tree-sha1 = "f3afd2d58e1f6ac9be2cea46e4a9083ccc1d990b" 423 | uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" 424 | version = "0.3.0" 425 | 426 | [[PlotUtils]] 427 | deps = ["Colors", "Dates", "Printf", "Random", "Reexport", "Test"] 428 | git-tree-sha1 = "8e87bbb778c26f575fbe47fd7a49c7b5ca37c0c6" 429 | uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" 430 | version = "0.5.8" 431 | 432 | [[Plots]] 433 | deps = ["Base64", "Contour", "Dates", "FixedPointNumbers", "GR", "JSON", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "Printf", "REPL", "Random", "RecipesBase", "Reexport", "Requires", "Showoff", "SparseArrays", "StaticArrays", "Statistics", "StatsBase", "Test", "UUIDs"] 434 | git-tree-sha1 = "5bcc6dbc8b7fe0823d9a63dde87d7d4542149693" 435 | uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" 436 | version = "0.24.0" 437 | 438 | [[Printf]] 439 | deps = ["Unicode"] 440 | uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" 441 | 442 | [[Profile]] 443 | deps = ["Printf"] 444 | uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79" 445 | 446 | [[PyCall]] 447 | deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Pkg", "Serialization", "Statistics", "Test", "VersionParsing"] 448 | git-tree-sha1 = "6e5bac1b1faf3575731a6a5b76f638f2389561d3" 449 | uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" 450 | version = "1.91.2" 451 | 452 | [[QuAlgorithmZoo]] 453 | deps = ["BitBasis", "DiffEqBase", "FFTW", "LinearAlgebra", "LuxurySparse", "MacroTools", "SparseArrays", "StatsBase", "Yao", "YaoArrayRegister", "YaoBlocks"] 454 | git-tree-sha1 = "10d05ccd1032ed269a15b0c5fc4077dbf98460e4" 455 | repo-rev = "master" 456 | repo-url = "https://github.com/QuantumBFS/QuAlgorithmZoo.jl.git" 457 | uuid = "65c24e16-9b0a-11e8-1353-efc5bc5f6586" 458 | version = "0.1.0" 459 | 460 | [[REPL]] 461 | deps = ["InteractiveUtils", "Markdown", "Sockets"] 462 | uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" 463 | 464 | [[Random]] 465 | deps = ["Serialization"] 466 | uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" 467 | 468 | [[RecipesBase]] 469 | deps = ["Random", "Test"] 470 | git-tree-sha1 = "0b3cb370ee4dc00f47f1193101600949f3dcf884" 471 | uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" 472 | version = "0.6.0" 473 | 474 | [[RecursiveArrayTools]] 475 | deps = ["ArrayInterface", "RecipesBase", "Requires", "StaticArrays", "Statistics", "Test"] 476 | git-tree-sha1 = "187ea7dd541955102c7035a6668613bdf52022ca" 477 | uuid = "731186ca-8d62-57ce-b412-fbd966d074cd" 478 | version = "0.20.0" 479 | 480 | [[RecursiveFactorization]] 481 | deps = ["LinearAlgebra", "Random", "Test"] 482 | git-tree-sha1 = "54410ebd72cbb84d7b7678eb3da643f8e71181fc" 483 | uuid = "f2c3362d-daeb-58d1-803e-2bc74f2840b4" 484 | version = "0.0.1" 485 | 486 | [[Reexport]] 487 | deps = ["Pkg"] 488 | git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0" 489 | uuid = "189a3867-3050-52da-a836-e630ba90ab69" 490 | version = "0.2.0" 491 | 492 | [[Requires]] 493 | deps = ["Test"] 494 | git-tree-sha1 = "f6fbf4ba64d295e146e49e021207993b6b48c7d1" 495 | uuid = "ae029012-a4dd-5104-9daa-d747884805df" 496 | version = "0.5.2" 497 | 498 | [[Revise]] 499 | deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "UUIDs", "Unicode"] 500 | git-tree-sha1 = "84f29e1b3670ade5c1b4f6fc80eac166bd37bb59" 501 | uuid = "295af30f-e4ad-537b-8983-00126c2a3abe" 502 | version = "2.1.3" 503 | 504 | [[Roots]] 505 | deps = ["Printf", "Statistics", "Test"] 506 | git-tree-sha1 = "7228278e31d6d0e22a1ae0b41ea9a0df2859f33d" 507 | uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" 508 | version = "0.8.1" 509 | 510 | [[SHA]] 511 | uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" 512 | 513 | [[Serialization]] 514 | uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" 515 | 516 | [[SharedArrays]] 517 | deps = ["Distributed", "Mmap", "Random", "Serialization"] 518 | uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" 519 | 520 | [[Showoff]] 521 | deps = ["Compat"] 522 | git-tree-sha1 = "276b24f3ace98bec911be7ff2928d497dc759085" 523 | uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f" 524 | version = "0.2.1" 525 | 526 | [[SimpleTraits]] 527 | deps = ["InteractiveUtils", "MacroTools", "Test"] 528 | git-tree-sha1 = "c0a542b8d5e369b179ccd296b2ca987f6da5da0a" 529 | uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d" 530 | version = "0.8.0" 531 | 532 | [[Sockets]] 533 | uuid = "6462fe0b-24de-5631-8697-dd941f90decc" 534 | 535 | [[SoftGlobalScope]] 536 | deps = ["Test"] 537 | git-tree-sha1 = "012661b70364840fcd380912d878d96f7bf95ff3" 538 | uuid = "b85f4697-e234-5449-a836-ec8e2f98b302" 539 | version = "1.0.10" 540 | 541 | [[SortingAlgorithms]] 542 | deps = ["DataStructures", "Random", "Test"] 543 | git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd" 544 | uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" 545 | version = "0.3.1" 546 | 547 | [[SparseArrays]] 548 | deps = ["LinearAlgebra", "Random"] 549 | uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" 550 | 551 | [[SpecialFunctions]] 552 | deps = ["BinDeps", "BinaryProvider", "Libdl", "Test"] 553 | git-tree-sha1 = "0b45dc2e45ed77f445617b99ff2adf0f5b0f23ea" 554 | uuid = "276daf66-3868-5448-9aa4-cd146d93841b" 555 | version = "0.7.2" 556 | 557 | [[StaticArrays]] 558 | deps = ["InteractiveUtils", "LinearAlgebra", "Random", "Statistics", "Test"] 559 | git-tree-sha1 = "3841b39ed5f047db1162627bf5f80a9cd3e39ae2" 560 | uuid = "90137ffa-7385-5640-81b9-e52037218182" 561 | version = "0.10.3" 562 | 563 | [[Statistics]] 564 | deps = ["LinearAlgebra", "SparseArrays"] 565 | uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" 566 | 567 | [[StatsBase]] 568 | deps = ["DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"] 569 | git-tree-sha1 = "8a0f4b09c7426478ab677245ab2b0b68552143c7" 570 | uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" 571 | version = "0.30.0" 572 | 573 | [[SuiteSparse]] 574 | deps = ["Libdl", "LinearAlgebra", "SparseArrays"] 575 | uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" 576 | 577 | [[TableTraits]] 578 | deps = ["IteratorInterfaceExtensions"] 579 | git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e" 580 | uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" 581 | version = "1.0.0" 582 | 583 | [[Test]] 584 | deps = ["Distributed", "InteractiveUtils", "Logging", "Random"] 585 | uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" 586 | 587 | [[TimerOutputs]] 588 | deps = ["Crayons", "Printf", "Test", "Unicode"] 589 | git-tree-sha1 = "b80671c06f8f8bae08c55d67b5ce292c5ae2660c" 590 | uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" 591 | version = "0.5.0" 592 | 593 | [[Tokenize]] 594 | deps = ["Printf", "Test"] 595 | git-tree-sha1 = "3e83f60b74911d3042d3550884ca2776386a02b8" 596 | uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624" 597 | version = "0.5.3" 598 | 599 | [[Tracker]] 600 | deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics", "Test"] 601 | git-tree-sha1 = "0bec1b68c63a0e8a58d3944261cbf4cc9577c8a1" 602 | uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" 603 | version = "0.2.0" 604 | 605 | [[TranscodingStreams]] 606 | deps = ["Random", "Test"] 607 | git-tree-sha1 = "a25d8e5a28c3b1b06d3859f30757d43106791919" 608 | uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" 609 | version = "0.9.4" 610 | 611 | [[TreeViews]] 612 | deps = ["Test"] 613 | git-tree-sha1 = "8d0d7a3fe2f30d6a7f833a5f19f7c7a5b396eae6" 614 | uuid = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7" 615 | version = "0.3.0" 616 | 617 | [[TupleTools]] 618 | deps = ["Random", "Test"] 619 | git-tree-sha1 = "b006524003142128cc6d36189dce337729aa0050" 620 | uuid = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6" 621 | version = "1.1.0" 622 | 623 | [[URIParser]] 624 | deps = ["Test", "Unicode"] 625 | git-tree-sha1 = "6ddf8244220dfda2f17539fa8c9de20d6c575b69" 626 | uuid = "30578b45-9adc-5946-b283-645ec420af67" 627 | version = "0.4.0" 628 | 629 | [[UUIDs]] 630 | deps = ["Random", "SHA"] 631 | uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" 632 | 633 | [[Unicode]] 634 | uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" 635 | 636 | [[VersionParsing]] 637 | deps = ["Compat"] 638 | git-tree-sha1 = "c9d5aa108588b978bd859554660c8a5c4f2f7669" 639 | uuid = "81def892-9a0e-5fdd-b105-ffc91e053289" 640 | version = "1.1.3" 641 | 642 | [[WebIO]] 643 | deps = ["AssetRegistry", "Base64", "Compat", "Distributed", "FunctionalCollections", "JSON", "Logging", "Observables", "Random", "Requires", "Sockets", "Test", "UUIDs", "Widgets"] 644 | git-tree-sha1 = "125dc746e5b36424c6a7e694889a7f3d434a160a" 645 | uuid = "0f1e0344-ec1d-5b48-a673-e5cf874b6c29" 646 | version = "0.8.1" 647 | 648 | [[Widgets]] 649 | deps = ["Colors", "Dates", "Observables", "OrderedCollections", "Test"] 650 | git-tree-sha1 = "c53befc70c6b91eaa2a9888c2f6ac2d92720a81b" 651 | uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62" 652 | version = "0.6.1" 653 | 654 | [[Yao]] 655 | deps = ["BitBasis", "Reexport", "YaoArrayRegister", "YaoBase", "YaoBlocks"] 656 | git-tree-sha1 = "3e8b2f69e70ac83778c77d097cdb3b0b116c17b8" 657 | repo-rev = "master" 658 | repo-url = "https://github.com/QuantumBFS/Yao.jl.git" 659 | uuid = "5872b779-8223-5990-8dd0-5abbb0748c8c" 660 | version = "0.4.2" 661 | 662 | [[YaoArrayRegister]] 663 | deps = ["BitBasis", "LinearAlgebra", "LuxurySparse", "StaticArrays", "StatsBase", "TupleTools", "YaoBase"] 664 | git-tree-sha1 = "e87a6f0dd4f68cdac1629fc2eeb5d2423faa47f1" 665 | repo-rev = "master" 666 | repo-url = "https://github.com/QuantumBFS/YaoArrayRegister.jl.git" 667 | uuid = "e600142f-9330-5003-8abb-0ebd767abc51" 668 | version = "0.3.7" 669 | 670 | [[YaoBase]] 671 | deps = ["BitBasis", "LegibleLambdas", "LinearAlgebra", "LuxurySparse", "MLStyle", "MacroTools", "SparseArrays", "Test", "TupleTools"] 672 | git-tree-sha1 = "0bbe0640e5cb2d1998f3fc32b939095e78199ca6" 673 | uuid = "a8f54c17-34bc-5a9d-b050-f522fe3f755f" 674 | version = "0.9.1" 675 | 676 | [[YaoBlocks]] 677 | deps = ["BitBasis", "CacheServers", "ExponentialUtilities", "LegibleLambdas", "LinearAlgebra", "LinearMaps", "LuxurySparse", "MLStyle", "Random", "SimpleTraits", "SparseArrays", "StaticArrays", "StatsBase", "YaoArrayRegister", "YaoBase"] 678 | git-tree-sha1 = "53a9668a425e2d83e1946b21445ed5266a952dc9" 679 | repo-rev = "master" 680 | repo-url = "https://github.com/QuantumBFS/YaoBlocks.jl.git" 681 | uuid = "418bc28f-b43b-5e0b-a6e7-61bbc1a2c1df" 682 | version = "0.3.4" 683 | 684 | [[ZMQ]] 685 | deps = ["BinaryProvider", "FileWatching", "Libdl", "Sockets", "Test"] 686 | git-tree-sha1 = "34e7ac2d1d59d19d0e86bde99f1f02262bfa1613" 687 | uuid = "c2297ded-f4af-51ae-bb23-16f91089e4e1" 688 | version = "1.0.0" 689 | 690 | [[ZipFile]] 691 | deps = ["BinaryProvider", "Libdl", "Printf", "Test"] 692 | git-tree-sha1 = "5f6f663890dfb9bad6af75a86a43f67904e5050e" 693 | uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" 694 | version = "0.8.1" 695 | 696 | [[Zygote]] 697 | deps = ["DiffRules", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics"] 698 | git-tree-sha1 = "ac4a5b2d7a6f2b2925a64150d46479a558c26667" 699 | repo-rev = "master" 700 | repo-url = "https://github.com/FluxML/Zygote.jl.git" 701 | uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" 702 | version = "0.3.0" 703 | -------------------------------------------------------------------------------- /Project.toml: -------------------------------------------------------------------------------- 1 | name = "SSSS" 2 | uuid = "038cdfb8-671c-11e9-3f17-89122373f6c7" 3 | version = "0.1.0" 4 | 5 | [deps] 6 | FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341" 7 | IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" 8 | Interact = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1" 9 | Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" 10 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning and Quantum Programming: A Spring School 2 | 3 | Song Shan Lake Spring School, features lectures, code challenge, install party and happy fatty night. 4 | 5 | *South Bay Interdisciplinary Science Center, Songshan Lake Materials Laboratory, Dongguan, China, 5th-10th May, 2019* 6 | 7 | ## Table of Contents 8 | 1. Deep Learning 9 | * [`lecture_notes.pdf`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/lecture_notes.pdf) and [`slides/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/slides) 10 | * Demo codes 11 | * Poor man's computation graph: [`computation_graph.py`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/computation_graph.py) 12 | * Variational free energy with flow model: [`realnvp/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/realnvp) 13 | * Hamiltonian inverse design with reverse mode AD: [`schrodinger.py`](https://github.com/QuantumBFS/SSSS/blob/master/1_deep_learning/schrodinger.py) 14 | * Solving the fastest descent problem with NeuralODE [`brachistochrone/`](https://github.com/QuantumBFS/SSSS/tree/master/1_deep_learning/brachistochrone) 15 | 2. Tensor Networks 16 | * [`Slides on tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/Tutorial_tensor_network.pdf) 17 | * [`Slides on contraction methods for infinite tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/tensor_contraction_methods.pdf) 18 | * [`Tutorial and demo codes on computing $2$-D Ising model partition function using tensor networks`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/tensor_contraction_simple.ipynb) 19 | * [`Tutorial and demo codes on the MPS Born machine`](https://github.com/QuantumBFS/SSSS/blob/master/2_tensor_network/mps_tutorial.ipynb) 20 | 3. Julia language 21 | * [`julia-hands-on.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/3_julia/julia-hands-on.ipynb) 22 | 4. Quantum Computing with Yao.jl 23 | * Lecture Note: [`quantum_lecture_note.pdf`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/quantum_lecture_note.pdf) 24 | * Slides: [`google slides`](https://docs.google.com/presentation/d/1jUTpa8pB3jEOWDW1U0rDTDQ-kpri8j8S4y77GQCo3iM/edit?usp=sharing) 25 | * Notebooks 26 | * The solution to the graph embeding problem: [`graph_embeding.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/graph_embeding.ipynb) 27 | * Quantum circuit computing with Yao.jl: [`QC-with-Yao.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/QC-with-Yao.ipynb), [`Yao-talk`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/yao-talk-2019.pdf) 28 | * Landscape of a quantum circuit: [`variational_quantum_circuit.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) 29 | * Variational quantum eigensolver: [`variational_quantum_circuit.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) 30 | * Matrix Product state inspired variational quantum eigensolver [`VQE_action.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/VQE_action.ipynb) 31 | * Quantum circuit born machine: [`qcbm_gaussian.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/qcbm_gaussian.ipynb) 32 | * Gradient vanishing problem: [`variational_quantum_circuit.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/variational_quantum_circuit.ipynb) and [`VQE_action.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/VQE_action.ipynb) 33 | * Mapping a quantum circuit to tensor networks: [`qc_tensor_mapping.ipynb`](https://github.com/QuantumBFS/SSSS/blob/master/4_quantum/qc_tensor_mapping.ipynb) 34 | 35 | Welcome for pull requests and issues! 36 | 37 | 38 | ## Challenge 39 | 40 | [Song-Shan-Hu Sping School Coding Challenge](Challenge.md) 41 | 42 | ## Preparation 43 | 44 | ### Quick start 45 | 46 | - [quick start for git](http://rogerdudler.github.io/git-guide/) 47 | - [quick start for command line interface](https://www.makeuseof.com/tag/a-quick-guide-to-get-started-with-the-linux-command-line/) 48 | 49 | ### Installation 50 | - [how to install ubuntu](https://tutorials.ubuntu.com/tutorial/tutorial-install-ubuntu-desktop) 51 | - [install annaconda](https://www.anaconda.com/distribution/) 52 | - [install PyTorch](https://pytorch.org/) 53 | - [install Julia](https://julialang.org) 54 | - [intall Yao.jl](https://github.com/QuantumBFS/Yao.jl#installation) 55 | 56 | ### Julia 57 | - [Julia语言的中文教程](https://github.com/Roger-luo/TutorialZH.jl) 58 | - [快速入门 Julia 语言](https://www.bilibili.com/video/av28248187?from=search&seid=5171149583764025744) 59 | - [Julia入门指引](https://discourse.juliacn.com/t/topic/159) 60 | 61 | 62 | ## Usage 63 | 64 | You can open this repo as a Julia package if you have julia installed: 65 | 66 | 1. open your Julia REPL, press `]` 67 | 2. type the following 68 | 69 | ```julia 70 | (1.0) pkg> add https://github.com/QuantumBFS/SSSS.git 71 | ``` 72 | 73 | 3. press backspace 74 | 4. type the following 75 | 76 | ```julia 77 | julia> using SSSS 78 | 79 | julia> notebooks() 80 | ``` 81 | 82 | ## License 83 | 84 | The code is released under MIT License. The rest part is released under [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) 85 | 86 |

87 | poster 88 |

89 | -------------------------------------------------------------------------------- /_assets/SongShanHu2019.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/_assets/SongShanHu2019.jpeg -------------------------------------------------------------------------------- /_assets/SongShanHu2019.key: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/_assets/SongShanHu2019.key -------------------------------------------------------------------------------- /_assets/c60.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/QuantumBFS/SSSS/b75ecbbfc19a954816164e7081737bd152f48070/_assets/c60.jpg -------------------------------------------------------------------------------- /src/SSSS.jl: -------------------------------------------------------------------------------- 1 | module SSSS 2 | 3 | using IJulia, Pkg 4 | 5 | export notebooks 6 | 7 | # function tutorial() 8 | # cmd = IJulia.find_jupyter_subcommand("notebook") 9 | # push!(cmd.exec, joinpath(@__DIR__, "..", "notebooks", "tutorial.ipynb")) 10 | # return IJulia.launch(cmd, joinpath(@__DIR__, "..", "notebooks"), false) 11 | # end 12 | 13 | function notebooks() 14 | return IJulia.notebook(dir=joinpath(@__DIR__, "..")) 15 | end 16 | 17 | # REQUIRE = [ 18 | # "GR", 19 | # "PyCall", 20 | # "IJulia", 21 | # "Revise", 22 | # "Plots", 23 | # "Latexify", 24 | # "FFTW", 25 | # PackageSpec(name="Flux", rev="master"), 26 | # "BitBasis", 27 | # "KrylovKit", 28 | # PackageSpec(url="https://github.com/QuantumBFS/QuAlgorithmZoo.jl.git", rev="master"), 29 | # PackageSpec(name="IRTools", rev="master"), 30 | # PackageSpec(name="NNlib", rev="master"), 31 | # PackageSpec(name="Zygote", rev="master"), 32 | # PackageSpec(name="Yao", rev="master"), 33 | # PackageSpec(name="YaoBlocks", rev="master"), 34 | # PackageSpec(name="YaoArrayRegister", rev="master"), 35 | # ] 36 | 37 | # function __init__() 38 | # for each in REQUIRE 39 | # if each in keys(Pkg.installed()) 40 | # continue 41 | # else 42 | # Pkg.add(each) 43 | # end 44 | # end 45 | # end 46 | 47 | end # module 48 | --------------------------------------------------------------------------------