├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE
├── README.md
├── application
└── banchmark_qsvm_tnsm-mpi_app.py
├── benchmark
├── README.md
├── banchmark_qsvm_svsm_cusvaer.py
├── banchmark_qsvm_tnsm-mpi_mgpu.py
├── banchmark_qsvm_tnsm-mpi_sgpu.py
├── banchmark_qsvm_tnsm-opt_einsum.py
├── banchmark_qsvm_tnsm.py
├── figure
│ ├── figure1_sgpu.png
│ ├── figure2_mgpu_v100.png
│ ├── figure3_mgpu_h100.png
│ └── figure_sgpu.png
└── mpi_demo.sh
├── cutn-qsvm.ipynb
├── env_check.py
├── environment.yml
├── figures
├── cutensornet_module.png
├── multi_GPU_linearity.png
├── multi_gpu_resource.png
├── process_flow_comparison.png
└── speedup_cutensornet.png
├── requirements.txt
└── requirements_benchmark.txt
/.gitignore:
--------------------------------------------------------------------------------
1 | env
2 | tutorial
--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
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/README.md:
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1 | [](https://opensource.org/licenses/Apache-2.0)
2 | [](https://arxiv.org/abs/2405.02630)
3 |
4 | ## cuTN-QSVM: cuTensorNet-accelerated Quantum Support Vector Machine with cuQuantum SDK
5 |
6 | 
9 |
10 | Welcome to the official repository of cuTN-QSVM, featuring fast GPU simulators for benchmarking Quantum Support Vector Machines (QSVMs) and scripts for generating compatible quantum circuits for hardware execution. Facilitated by NVIDIA's [cuQuantum SDK](https://github.com/NVIDIA/cuda-quantum/tree/main) and the [cuTensorNet](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/overview.html) library, this project integrates cutting-edge quantum computing technologies with high-performance computing systems, enhancing quantum machine learning's efficiency and scalability to new heights.
11 |
12 | ## Project Overview
13 | Quantum Support Vector Machines (QSVMs) utilize a quantum-enhanced approach to tackle complex, multidimensional classification problems, surpassing the capabilities of classical SVMs under certain conditions. However, prior to the advent of large-scale quantum systems, the scalability of simulating QSVMs on CPUs was traditionally limited by the exponential growth in computational demands as qubit counts increased. By employing NVIDIA's cuQuantum SDK and the cuTensorNet library, cuTN-QSVM effectively reduces this computational complexity from exponential to quadratic. This enables the simulation of large quantum systems of up to 784 qubits on the NVIDIA A100 GPU within seconds.
14 |
15 | Technical Highlights:
16 |
17 | - Efficient Quantum Simulations: The cuTensorNet library significantly lowers the computational overhead for QSVMs, facilitating rapid and efficient quantum simulations that can handle extensive qubit counts.
18 | - Multi-GPU Processing: Supported by the Message Passing Interface (MPI), our implementation allows significant reductions in computation times and scalable performance improvements across varying data sizes.
19 | - Empirical Validation: Through rigorous testing, cuTN-QSVM achieves high classification accuracy, with results reaching up to 95% on the MNIST dataset for training sets larger than 100 instances, markedly outperforming traditional SVMs.
20 |
21 |
22 |
23 |
24 |
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