├── .DS_Store ├── Assets ├── .DS_Store ├── overview.png ├── conv_ansatz.png ├── hwe_ansatz.png ├── sea_ansatz.png ├── depth_ansatz.png ├── conv_gen_pseudocode.png ├── generator_ansatzes.png ├── hwe_gen_pseudocode.png ├── sea_gen_pseudocode.png ├── depth_learning_hwe_pseudocode.png └── two_qubit_unitary_pseudocode.png ├── Convolutional ├── .DS_Store ├── 3_Qubits │ ├── .DS_Store │ ├── Depth_1 │ │ ├── conv_3q_ps_5_1_weights.npy │ │ ├── conv_3q_ps_15_1_weights.npy │ │ ├── conv_3q_ps_25_1_weights.npy │ │ ├── conv_3q_ps_35_1_weights.npy │ │ ├── conv_3q_ps_5_1_weights.txt │ │ ├── conv_3q_ps_15_1_weights.txt │ │ ├── conv_3q_ps_25_1_weights.txt │ │ └── conv_3q_ps_35_1_weights.txt │ ├── Depth_2 │ │ ├── conv_3q_ps_5_2_weights.npy │ │ ├── conv_3q_ps_15_2_weights.npy │ │ ├── conv_3q_ps_25_2_weights.npy │ │ ├── conv_3q_ps_35_2_weights.npy │ │ ├── conv_3q_ps_35_2_weights.txt │ │ ├── conv_3q_ps_15_2_weights.txt │ │ ├── conv_3q_ps_25_2_weights.txt │ │ └── conv_3q_ps_5_2_weights.txt │ ├── Depth_3 │ │ ├── conv_3q_ps_5_3_weights.npy │ │ ├── conv_3q_ps_15_3_weights.npy │ │ ├── conv_3q_ps_25_3_weights.npy │ │ ├── conv_3q_ps_35_3_weights.npy │ │ ├── conv_3q_ps_25_3_weights.txt │ │ ├── conv_3q_ps_15_3_weights.txt │ │ ├── conv_3q_ps_5_3_weights.txt │ │ └── conv_3q_ps_35_3_weights.txt │ ├── Depth_4 │ │ ├── conv_3q_ps_5_4_weights.npy │ │ ├── conv_3q_ps_15_4_weights.npy │ │ ├── conv_3q_ps_25_4_weights.npy │ │ ├── conv_3q_ps_35_4_weights.npy │ │ ├── conv_3q_ps_35_4_weights.txt │ │ ├── conv_3q_ps_25_4_weights.txt │ │ ├── conv_3q_ps_5_4_weights.txt │ │ └── conv_3q_ps_15_4_weights.txt │ ├── Depth_5 │ │ ├── conv_3q_ps_5_5_weights.npy │ │ ├── conv_3q_ps_15_5_weights.npy │ │ ├── conv_3q_ps_25_5_weights.npy │ │ ├── conv_3q_ps_35_5_weights.npy │ │ ├── conv_3q_ps_35_5_weights.txt │ │ ├── conv_3q_ps_15_5_weights.txt │ │ ├── conv_3q_ps_25_5_weights.txt │ │ └── conv_3q_ps_5_5_weights.txt │ └── Depth_6 │ │ ├── conv_3q_ps_5_6_weights.npy │ │ ├── conv_3q_ps_15_6_weights.npy │ │ ├── conv_3q_ps_25_6_weights.npy │ │ ├── conv_3q_ps_35_6_weights.npy │ │ ├── conv_3q_ps_25_6_weights.txt │ │ ├── conv_3q_ps_5_6_weights.txt │ │ ├── conv_3q_ps_15_6_weights.txt │ │ └── conv_3q_ps_35_6_weights.txt └── README.md ├── Hardware_Efficient ├── .DS_Store ├── 8_Qubits │ ├── README.md │ ├── .DS_Store │ ├── Depth_5 │ │ ├── hwe_8q_cb_15_5_weights.npy │ │ ├── hwe_8q_cb_40_5_weights.npy │ │ ├── hwe_8q_cb_45_5_weights.npy │ │ ├── hwe_8q_cb_15_5_tangle_weights.npy │ │ ├── hwe_8q_cb_40_5_weights.txt │ │ ├── hwe_8q_cb_45_5_weights.txt │ │ ├── hwe_8q_cb_15_5_weights.txt │ │ └── hwe_8q_cb_15_5_tangle_weights.txt │ └── Depth_6 │ │ ├── hwe_8q_cb_10_6_weights.npy │ │ ├── hwe_8q_cb_25_6_weights.npy │ │ ├── hwe_8q_cb_25_6_weights.txt │ │ └── hwe_8q_cb_10_6_weights.txt ├── 3_Qubits │ ├── .DS_Store │ ├── Depth_6 │ │ ├── .DS_Store │ │ ├── hwe_3q_ps_15_6_weights.npy │ │ ├── hwe_3q_ps_25_6_weights.npy │ │ ├── hwe_3q_ps_35_6_weights.npy │ │ ├── hwe_3q_ps_5_6_weights.npy │ │ ├── hwe_3q_ps_5_6_weights.txt │ │ ├── hwe_3q_ps_25_6_weights.txt │ │ ├── hwe_3q_ps_35_6_weights.txt │ │ └── hwe_3q_ps_15_6_weights.txt │ ├── Depth_1 │ │ ├── hwe_3q_ps_15_1_weights.npy │ │ ├── hwe_3q_ps_25_1_weights.npy │ │ ├── hwe_3q_ps_35_1_weights.npy │ │ ├── hwe_3q_ps_5_1_weights.npy │ │ ├── hwe_3q_ps_25_1_weights.txt │ │ ├── hwe_3q_ps_15_1_weights.txt │ │ ├── hwe_3q_ps_35_1_weights.txt │ │ └── hwe_3q_ps_5_1_weights.txt │ ├── Depth_2 │ │ ├── hwe_3q_ps_15_2_weights.npy │ │ ├── hwe_3q_ps_25_2_weights.npy │ │ ├── hwe_3q_ps_35_2_weights.npy │ │ ├── hwe_3q_ps_5_2_weights.npy │ │ ├── hwe_3q_ps_35_2_weights.txt │ │ ├── hwe_3q_ps_15_2_weights.txt │ │ ├── hwe_3q_ps_5_2_weights.txt │ │ └── hwe_3q_ps_25_2_weights.txt │ ├── Depth_3 │ │ ├── hwe_3q_ps_15_3_weights.npy │ │ ├── hwe_3q_ps_25_3_weights.npy │ │ ├── hwe_3q_ps_35_3_weights.npy │ │ ├── hwe_3q_ps_5_3_weights.npy │ │ ├── hwe_3q_ps_25_3_weights.txt │ │ ├── hwe_3q_ps_5_3_weights.txt │ │ ├── hwe_3q_ps_15_3_weights.txt │ │ └── hwe_3q_ps_35_3_weights.txt │ ├── Depth_4 │ │ ├── hwe_3q_ps_15_4_weights.npy │ │ ├── hwe_3q_ps_25_4_weights.npy │ │ ├── hwe_3q_ps_35_4_weights.npy │ │ ├── hwe_3q_ps_5_4_weights.npy │ │ ├── hwe_3q_ps_25_4_weights.txt │ │ ├── hwe_3q_ps_5_4_weights.txt │ │ ├── hwe_3q_ps_35_4_weights.txt │ │ └── hwe_3q_ps_15_4_weights.txt │ └── Depth_5 │ │ ├── hwe_3q_ps_15_5_weights.npy │ │ ├── hwe_3q_ps_25_5_weights.npy │ │ ├── hwe_3q_ps_35_5_weights.npy │ │ ├── hwe_3q_ps_5_5_weights.npy │ │ ├── hwe_3q_ps_5_5_weights.txt │ │ ├── hwe_3q_ps_15_5_weights.txt │ │ ├── hwe_3q_ps_25_5_weights.txt │ │ └── hwe_3q_ps_35_5_weights.txt ├── 4_Qubits │ ├── .DS_Store │ ├── Depth_1 │ │ ├── hwe_4q_ps_15_1_weights.npy │ │ ├── hwe_4q_ps_25_1_weights.npy │ │ ├── hwe_4q_ps_35_1_weights.npy │ │ ├── hwe_4q_ps_5_1_weights.npy │ │ ├── hwe_4q_ps_35_1_weights.txt │ │ ├── hwe_4q_ps_15_1_weights.txt │ │ ├── hwe_4q_ps_25_1_weights.txt │ │ └── hwe_4q_ps_5_1_weights.txt │ ├── Depth_2 │ │ ├── hwe_4q_ps_15_2_weights.npy │ │ ├── hwe_4q_ps_25_2_weights.npy │ │ ├── hwe_4q_ps_35_2_weights.npy │ │ ├── hwe_4q_ps_5_2_weights.npy │ │ ├── hwe_4q_ps_25_2_weights.txt │ │ ├── hwe_4q_ps_35_2_weights.txt │ │ ├── hwe_4q_ps_15_2_weights.txt │ │ └── hwe_4q_ps_5_2_weights.txt │ ├── Depth_3 │ │ ├── hwe_4q_ps_15_3_weights.npy │ │ ├── hwe_4q_ps_25_3_weights.npy │ │ ├── hwe_4q_ps_35_3_weights.npy │ │ ├── hwe_4q_ps_5_3_weights.npy │ │ ├── hwe_4q_ps_5_3_weights.txt │ │ ├── hwe_4q_ps_15_3_weights.txt │ │ ├── hwe_4q_ps_25_3_weights.txt │ │ └── hwe_4q_ps_35_3_weights.txt │ ├── Depth_4 │ │ ├── hwe_4q_ps_15_4_weights.npy │ │ ├── hwe_4q_ps_25_4_weights.npy │ │ ├── hwe_4q_ps_35_4_weights.npy │ │ ├── hwe_4q_ps_5_4_weights.npy │ │ ├── hwe_4q_ps_35_4_weights.txt │ │ ├── hwe_4q_ps_15_4_weights.txt │ │ ├── hwe_4q_ps_25_4_weights.txt │ │ └── hwe_4q_ps_5_4_weights.txt │ ├── Depth_5 │ │ ├── hwe_4q_ps_15_5_weights.npy │ │ ├── hwe_4q_ps_25_5_weights.npy │ │ ├── hwe_4q_ps_35_5_weights.npy │ │ ├── hwe_4q_ps_5_5_weights.npy │ │ ├── hwe_4q_ps_35_5_weights.txt │ │ ├── hwe_4q_ps_25_5_weights.txt │ │ ├── hwe_4q_ps_15_5_weights.txt │ │ └── hwe_4q_ps_5_5_weights.txt │ └── Depth_6 │ │ ├── hwe_4q_ps_15_6_weights.npy │ │ ├── hwe_4q_ps_25_6_weights.npy │ │ ├── hwe_4q_ps_35_6_weights.npy │ │ ├── hwe_4q_ps_5_6_weights.npy │ │ ├── hwe_4q_ps_25_6_weights.txt │ │ ├── hwe_4q_ps_5_6_weights.txt │ │ ├── hwe_4q_ps_35_6_weights.txt │ │ └── hwe_4q_ps_15_6_weights.txt └── README.md ├── Strongly_Entangling ├── .DS_Store ├── 3_Qubits │ ├── .DS_Store │ ├── Depth_1 │ │ ├── .DS_Store │ │ ├── sea_3q_ps_5_1_weights.npy │ │ ├── sea_3q_ps_15_1_weights.npy │ │ ├── sea_3q_ps_25_1_weights.npy │ │ ├── sea_3q_ps_35_1_weights.npy │ │ ├── sea_3q_ps_15_1_weights.txt │ │ ├── sea_3q_ps_25_1_weights.txt │ │ ├── sea_3q_ps_35_1_weights.txt │ │ └── sea_3q_ps_5_1_weights.txt │ ├── Depth_2 │ │ ├── .DS_Store │ │ ├── sea_3q_ps_5_2_weights.npy │ │ ├── sea_3q_ps_15_2_weights.npy │ │ ├── sea_3q_ps_25_2_weights.npy │ │ ├── sea_3q_ps_35_2_weights.npy │ │ ├── sea_3q_ps_15_2_weights.txt │ │ ├── sea_3q_ps_25_2_weights.txt │ │ ├── sea_3q_ps_35_2_weights.txt │ │ └── sea_3q_ps_5_2_weights.txt │ ├── Depth_6 │ │ ├── .DS_Store │ │ ├── sea_3q_ps_5_6_weights.npy │ │ ├── sea_3q_ps_15_6_weights.npy │ │ ├── sea_3q_ps_25_6_weights.npy │ │ ├── sea_3q_ps_35_6_weights.npy │ │ ├── sea_3q_ps_25_6_weights.txt │ │ ├── sea_3q_ps_35_6_weights.txt │ │ ├── sea_3q_ps_15_6_weights.txt │ │ └── sea_3q_ps_5_6_weights.txt │ ├── Depth_3 │ │ ├── sea_3q_ps_5_3_weights.npy │ │ ├── sea_3q_ps_15_3_weights.npy │ │ ├── sea_3q_ps_25_3_weights.npy │ │ ├── sea_3q_ps_35_3_weights.npy │ │ ├── sea_3q_ps_15_3_weights.txt │ │ ├── sea_3q_ps_35_3_weights.txt │ │ ├── sea_3q_ps_5_3_weights.txt │ │ └── sea_3q_ps_25_3_weights.txt │ ├── Depth_4 │ │ ├── sea_3q_ps_5_4_weights.npy │ │ ├── sea_3q_ps_15_4_weights.npy │ │ ├── sea_3q_ps_25_4_weights.npy │ │ ├── sea_3q_ps_35_4_weights.npy │ │ ├── sea_3q_ps_25_4_weights.txt │ │ ├── sea_3q_ps_15_4_weights.txt │ │ ├── sea_3q_ps_35_4_weights.txt │ │ └── sea_3q_ps_5_4_weights.txt │ └── Depth_5 │ │ ├── sea_3q_ps_5_5_weights.npy │ │ ├── sea_3q_ps_15_5_weights.npy │ │ ├── sea_3q_ps_25_5_weights.npy │ │ ├── sea_3q_ps_35_5_weights.npy │ │ ├── sea_3q_ps_15_5_weights.txt │ │ ├── sea_3q_ps_35_5_weights.txt │ │ ├── sea_3q_ps_5_5_weights.txt │ │ └── sea_3q_ps_25_5_weights.txt ├── 4_Qubits │ ├── .DS_Store │ ├── Depth_1 │ │ ├── sea_4q_ps_5_1_weights.npy │ │ ├── sea_4q_ps_15_1_weights.npy │ │ ├── sea_4q_ps_25_1_weights.npy │ │ ├── sea_4q_ps_35_1_weights.npy │ │ ├── sea_4q_ps_25_1_weights.txt │ │ ├── sea_4q_ps_35_1_weights.txt │ │ ├── sea_4q_ps_5_1_weights.txt │ │ └── sea_4q_ps_15_1_weights.txt │ ├── Depth_2 │ │ ├── sea_4q_ps_5_2_weights.npy │ │ ├── sea_4q_ps_15_2_weights.npy │ │ ├── sea_4q_ps_25_2_weights.npy │ │ ├── sea_4q_ps_35_2_weights.npy │ │ ├── sea_4q_ps_35_2_weights.txt │ │ ├── sea_4q_ps_5_2_weights.txt │ │ ├── sea_4q_ps_25_2_weights.txt │ │ └── sea_4q_ps_15_2_weights.txt │ ├── Depth_3 │ │ ├── sea_4q_ps_5_3_weights.npy │ │ ├── sea_4q_ps_15_3_weights.npy │ │ ├── sea_4q_ps_25_3_weights.npy │ │ ├── sea_4q_ps_35_3_weights.npy │ │ ├── sea_4q_ps_35_3_weights.txt │ │ ├── sea_4q_ps_15_3_weights.txt │ │ ├── sea_4q_ps_25_3_weights.txt │ │ └── sea_4q_ps_5_3_weights.txt │ ├── Depth_4 │ │ ├── sea_4q_ps_5_4_weights.npy │ │ ├── sea_4q_ps_15_4_weights.npy │ │ ├── sea_4q_ps_25_4_weights.npy │ │ ├── sea_4q_ps_35_4_weights.npy │ │ ├── sea_4q_ps_25_4_weights.txt │ │ ├── sea_4q_ps_35_4_weights.txt │ │ ├── sea_4q_ps_5_4_weights.txt │ │ └── sea_4q_ps_15_4_weights.txt │ ├── Depth_5 │ │ ├── sea_4q_ps_5_5_weights.npy │ │ ├── sea_4q_ps_15_5_weights.npy │ │ ├── sea_4q_ps_25_5_weights.npy │ │ ├── sea_4q_ps_35_5_weights.npy │ │ ├── sea_4q_ps_5_5_weights.txt │ │ ├── sea_4q_ps_15_5_weights.txt │ │ ├── sea_4q_ps_25_5_weights.txt │ │ └── sea_4q_ps_35_5_weights.txt │ └── Depth_6 │ │ ├── sea_4q_ps_5_6_weights.npy │ │ ├── sea_4q_ps_15_6_weights.npy │ │ ├── sea_4q_ps_25_6_weights.npy │ │ ├── sea_4q_ps_35_6_weights.npy │ │ ├── sea_4q_ps_35_6_weights.txt │ │ ├── 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/Hardware_Efficient/4_Qubits/Depth_1/hwe_4q_ps_5_1_weights.txt: -------------------------------------------------------------------------------- 1 | [[[-0.03804919690785822, 0.02183672413366615, 0.032205693015151235], [-0.04109687962551794, 0.0445848243445975, -0.014484145306056642], [0.042754080670193664, -0.045441974600554436, -0.02364965177172911], [-0.04332198689054907, -0.047739338124097745, -0.015987443877689266]]] -------------------------------------------------------------------------------- /Strongly_Entangling/4_Qubits/Depth_1/sea_4q_ps_15_1_weights.txt: -------------------------------------------------------------------------------- 1 | [[[0.03695402390253631, -0.018024158180943654, 0.044187131769193796], [-0.04135087920390106, -0.047418471929042895, 0.017905688737795813], [-0.029924983100592994, -0.021329494920463633, -0.013947256402165986], [-0.028369957020594083, -0.021977873865901755, 0.013616705862364895]]] -------------------------------------------------------------------------------- /Hardware_Efficient/README.md: -------------------------------------------------------------------------------- 1 | In the pseudocode below, CZ(i,j) indicates a controlled Z operation on registers i and j. U3 indicates an arbitray 1 qubit unitary on register i. 2 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/hwe_gen_pseudocode.png) 3 | 4 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/hwe_ansatz.png) 5 | -------------------------------------------------------------------------------- /Strongly_Entangling/README.md: -------------------------------------------------------------------------------- 1 | In the pseudocode below, CNOT(i,j) indicates a controlled not with register i as the control j as the target. U3 indicates an arbitray 1 qubit unitary on register i. 2 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/sea_gen_pseudocode.png) 3 | 4 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/sea_ansatz.png) 5 | -------------------------------------------------------------------------------- /Hardware_Efficient/3_Qubits/Depth_2/hwe_3q_ps_35_2_weights.txt: -------------------------------------------------------------------------------- 1 | [[[0.9324756767082443, 1.0081436811628903, -0.12154315649683489], [1.5548589446751806, -0.6712697016012638, 0.5708282768327803], [0.4955562087489147, -0.7033067134330648, -0.7302097618503868]], [[1.2757909364562212, 0.0431462865273236, 0.2875955982166713], [-1.5571431824954256, -0.6181319703968097, 0.4859864198163841], [1.0486446134307725, 0.9334602314491603, 0.4391038524078855]]] -------------------------------------------------------------------------------- /Hardware_Efficient/3_Qubits/Depth_2/hwe_3q_ps_15_2_weights.txt: -------------------------------------------------------------------------------- 1 | [[[-0.6480724124662699, 0.5853122404952219, 0.12308382825208877], [0.36245622867661287, 0.6627389189037725, 0.6837788080835026], [-0.5936012556188656, -0.3485595096051093, -0.30982334790511806]], [[-0.1541907073606729, -0.04687878976079549, 0.80085117341226], [-0.6107181093431971, 0.7729456894061858, 0.7398539215333412], [-0.31768074117189593, -0.04176019503213188, -0.35848046199421413]]] -------------------------------------------------------------------------------- /Hardware_Efficient/3_Qubits/Depth_2/hwe_3q_ps_5_2_weights.txt: -------------------------------------------------------------------------------- 1 | [[[0.7225194754721478, 2.276026203553524, -1.3937031337396215], [-0.2576721743669021, -3.402366990483594, -3.3717426468493756], [3.4919094044936756, -1.5707304696134923, -1.5737561713738772]], [[-0.5521281350789741, 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-------------------------------------------------------------------------------- 1 | [[[-0.46771516497482757, 1.3063092462677497, 0.7364779911014975], [0.222192369139387, 0.010052966048019817, 0.2478055932446587], [0.4601112375298848, 0.1351803535659744, 0.3046185475508688]], [[-0.6918700578890115, -0.9642258999571612, -1.1382176178581331], [-1.0379890926035018, -0.8090940983588123, -0.1631881295820847], [-1.0579841890855215, 0.7253456466780581, -1.1519948329167475]]] -------------------------------------------------------------------------------- /Hardware_Efficient/3_Qubits/Depth_2/hwe_3q_ps_25_2_weights.txt: -------------------------------------------------------------------------------- 1 | [[[1.0780920396525953, 0.35388558127273334, -1.0629076772602948], [-1.2470091941636379, -0.07875354269087775, -0.020294656913737823], [-0.6267816316368754, -0.11758721492176887, 0.6001064877838571]], [[0.9813180170457203, -0.0038439723782407826, 1.2714463020510731], [-1.166502794454525, 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0.4442459162105231, 1.9099403064588318], [-1.2119490232210224, 0.9136277970738301, -1.2975595322432958], [-1.2204830659884367, -1.2226657814681703, -0.3967136713958849]], [[-0.017545949417018262, -0.1654140382997671, 0.15153557487141153], [0.01718378298735482, 0.22611523900856823, -0.009651179230093255], [-0.08090264014635251, -0.03479340020006649, -0.03936907920130677]]] -------------------------------------------------------------------------------- /Convolutional/README.md: -------------------------------------------------------------------------------- 1 | In the pseudocode below, 2QU indicates an arbitrary 2 qubit unitary between registers i and j. U3 indicates an arbitray 1 qubit unitary on register i. 2 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/conv_gen_pseudocode.png) 3 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/two_qubit_unitary_pseudocode.png) 4 | 5 | ![test](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/conv_ansatz.png) 6 | -------------------------------------------------------------------------------- /Hardware_Efficient/4_Qubits/Depth_2/hwe_4q_ps_25_2_weights.txt: -------------------------------------------------------------------------------- 1 | [[[-0.7106023099787598, 0.8446591838978073, 0.6316074378916706], [-0.7299104043382828, 0.7539258270246655, 0.5216648570598151], [-0.5826967749371109, -0.8831273322729084, -0.9826801626471219], [0.5093185898165082, -0.8452033923415576, -0.3769886340406612]], [[-0.5802641644037821, 0.03723079361687163, 0.9921088822960671], [-0.6519479218384511, -0.3004798991502957, 0.8621530860314788], 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/Hardware_Efficient/3_Qubits/Depth_5/hwe_3q_ps_25_5_weights.txt: -------------------------------------------------------------------------------- 1 | [[[-0.6060886321095112, 0.08112107224079534, 0.027829602730099948], [1.2854867889358563, 0.2708918874840559, -0.4309974922541216], [-0.5364888842708535, -0.09804232867454349, -0.6361291575824856]], [[-0.4814322751652705, -0.002657962446580929, 0.06466776145334545], [-0.6730926966143588, 0.10868406306942158, 0.3540551301881832], [0.021186799906515865, 0.21280688795338323, 0.14565572129311719]], [[-0.447996348392423, -0.25300551290873674, -0.03508698678741399], [0.9708055249355483, 0.15272063334800498, 0.14738647764211696], [-0.4795471400520528, 0.308308883063413, 0.3166687286818403]], [[-0.46491385351031783, -0.44041195849153486, -0.4342960173209664], [-0.7673045345986759, -0.5210634873324358, -0.41801734043696687], [0.262508960103683, 0.3402713174698534, 0.3662109770895243]], [[-1.0474536101002099, 0.010022603259391293, 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-------------------------------------------------------------------------------- 1 | [[[0.8808233920776529, 1.5707082859558201, -0.5455466340184588], [0.2968889844557325, -0.3496972500541882, 0.1347834331858197], [-0.6090166877841147, -0.21621051157066273, -0.8028401126594137]], [[0.27674735480294116, 1.4370737896671124, -0.009900107973278973], [0.20777235156465126, 0.06789776593237365, 0.017925829283624666], [-0.20307015185810773, 1.3099297432936556, -0.024480904235880167]], [[-0.4521887650427771, -0.25782787582232713, 0.22440240499600228], [-0.056738898936038416, -0.4472723139438865, -0.198742039429201], [-0.011217011261212063, 1.203204766921021, -0.07524997736023697]], [[0.21972529333436172, -0.18982102440304183, -0.19266042219005522], [-0.007272682998515979, -0.10646703097337644, 0.3635631721454493], [0.8740534527805891, 0.01649086014775237, 0.7707641189036095]], [[0.7229540654008554, 0.06966954442395368, 0.7948508169231455], [0.2552036389994601, -1.3046950272091984, 0.14960541557042745], [-0.15766433789050355, 0.1152511906588429, 0.16270173735122034]]] -------------------------------------------------------------------------------- /Strongly_Entangling/3_Qubits/Depth_5/sea_3q_ps_25_5_weights.txt: -------------------------------------------------------------------------------- 1 | [[[-0.07476002448133579, -0.4123628759071272, -0.18615438834995424], [-0.27275928089983376, 0.12534605844482052, -0.15139643746303436], [-0.895838106173039, -0.6312345559661837, -0.21122702278995267]], [[0.34710186238934637, -0.10495962114661742, 0.2941306204930572], [-0.14123570556511317, -0.05442592607870294, -0.11718517116267023], [0.10153238164710394, -0.017866946192288263, 0.02241080031040071]], [[-0.30254161711328, -0.3861304312778722, -0.19214650691726648], [0.2434187144252381, -0.6565828072750826, 0.1824023838249379], [-0.290335936176856, -0.6074796963240036, -0.45839171946061674]], [[-0.09353519460678582, -0.5052040824408636, 0.043159787264858473], [-0.010073819241424427, -0.17558899238839293, -0.14552772363584765], [-0.10192447567220178, -0.18871694810693423, -0.0733862736738157]], [[-0.018052345511489744, 0.3443492976609489, 0.1806221890629339], [0.5417353279654127, -0.8251678990262608, 0.3252451984020729], [-0.12168332328079527, 0.7606600929382976, -0.20958189339930128]]] -------------------------------------------------------------------------------- /Pseudocode/README.md: -------------------------------------------------------------------------------- 1 | To fascilitate usage of this dataset we provide pseudocode for all ansatzes and operations within those ansatzes. 2 | 3 | First, some notation. CNOT(i,j) indicates a controlled not with register i as control and j as target. CZ(i,j) indicates a controlled Z on i and j. 2QU indicates an arbitrary 2 qubit unitary (see decomposition below), and U3 indicates an arbitrary 1 qubit unitary. 4 | 5 | We use the following single qubit unitary decomposition: 6 | U3(i;θ,ϕ,λ) = Rz(ϕ)Rx(-𝜋/2)Rz(θ)Rx(𝜋/2)Rz(λ) 7 | 8 | ![2qu](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/two_qubit_unitary_pseudocode.png) 9 | ![hwe](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/hwe_gen_pseudocode.png) 10 | ![sea](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/sea_gen_pseudocode.png) 11 | ![conv](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/conv_gen_pseudocode.png) 12 | ![depth_learning](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/depth_learning_hwe_pseudocode.png) 13 | -------------------------------------------------------------------------------- /Hardware_Efficient/4_Qubits/Depth_4/hwe_4q_ps_35_4_weights.txt: 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-0.45370811972569886, -0.4750101109492038, 0.7340020268693853, -0.48738679624737635, -0.4210954692176934, -1.2060699657442324, 0.45079811934531355, 0.4612789287046337, 0.1486520800711483, 0.2881130514775308, -0.3298477941097763, -0.03134194577497372, 0.006314157590397089, 0.0050412402087927095, 0.027256523018451197, -0.04700555660172014, -0.014419188117674385]] -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NTangled Datasets 2 | Entanglement-based quantum datasets and generators for quantum machine learning. Link to paper. 3 | 4 | ## Motivation 5 | High quality, large-scale, datasets have played a crucial role in the development and success of classical machine learning. While most proposed novel 6 | quantum machine learning (QML) architectures are benchmarked using classical datasets, 7 | there is still doubt if whether QML on classical datasets is the best path towards a quantum advantage. In our work (arxiv link here), we argue that one should 8 | instead employ quantum datasets composed of quantum states. For this purpose, we introduce the NTangled dataset composed of quantum states with 9 | different amounts and types of multipartite entanglement. 10 | 11 | We generate states by sending product state inputs into parameterized quantum circuits (a) and then classify them through quantum neural networks (b). 12 | 13 | State generation through parameterized quantum circuits and state classification through quantum convolutional neural networks. 14 | 15 | ## Data Formatting 16 | 17 | All datasets in this repository are provided in both Numpy (.npy) and txt formats. Loading from the .npy files is easy; simply perform 18 | 19 | `data = np.load('path/to/file')` 20 | 21 | Data in .txt files is stored in JSON formatting and can be loaded via 22 | 23 | `data = json.load('path/to/file')` 24 | 25 | Entangled state generator data files have the following naming convention: 'ansatz_qubits_inputtype_(goalCE\* 100)_depth_weights.npy(txt)' 26 | 27 | ## Entangled State Generation and Datasets 28 | Here we consider training a parameterized quantum circuit to output states with a desired amount of entanglement. To quantify entanglement we use the concentratable entanglement, a multipartite entanglement measure (arXiv:2104.06923). 29 | 30 | The datasets consist of trained weights for three ansatzes--hardware efficient (a), strongly entangling (b), and convolutional (c)--with various numbers of qubits, depths, and goal values of entanglement. 31 | 32 | To use this dataset download and load the desired weights. From there, feed states from the input distribution into a parameterized circuit using the provided parameters. The outputs will be entangled states. 33 | 34 | Further details about each ansatz, including proper input distributions and pseudocode for implementation, are provided in their respective subfolders. 35 | 36 | ![Ansatzes for Entangled State Generation](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/generator_ansatzes.png) 37 | 38 | ## Ansatz Depth Learning 39 | In this task one takes a state generated via some number of layers of an ansatz and tries to determine what depth the ansatz was. Note that the input is not a fixed state, but rather a random product state. We include pseudocode below for the ansatz. Note that CNOT(i,j) indicates a controlled not with register i as the control and j as the target. U3 indicates an arbitrary single qubit unitary on register i. 40 | 41 | ![Depth Pseudocode](https://github.com/LSchatzki/NTangled_Datasets/blob/main/Assets/depth_learning_hwe_pseudocode.png) 42 | -------------------------------------------------------------------------------- /Convolutional/3_Qubits/Depth_6/conv_3q_ps_25_6_weights.txt: -------------------------------------------------------------------------------- 1 | [[0.19353665299518136, -0.04810827475791644, -0.0416189835542297, -0.5307605862522028, 0.22207451469107234, 0.17719631358724675, 0.9781382243393454, -0.7481382762816925, 0.7683181608756025, -0.44714952618608855, 0.13194564512925094, 0.02731449794363192, -0.12123153369970811, 0.18674596654941464, 0.1636657264341711, -0.14399616032859164, 0.23689241986978943, 0.1280959842110394, 0.5663290441585486, 0.16708508264817779, 0.05910133903870053, -0.7043676382339982, 0.7103748352017667, -0.3296682253957678, 0.23070498067364317, 0.2385644889730448, 0.2762150197282342, 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