We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution of hybrid quantum-classical models and variational quantum algorithms on both CPUs and GPUs. For photonic quantum computing, DeepQuantum implements Fock, Gaussian, and Bosonic backends, catering to different simulation needs. To our knowledge, it is the first framework to realize closed-loop integration of three paradigms of quantum computing, namely quantum circuits, photonic quantum circuits, and measurement-based quantum computing, thereby enabling robust support for both specialized and universal photonic quantum algorithm design. Furthermore, DeepQuantum supports large-scale simulations based on tensor network techniques and a distributed parallel computing architecture. We demonstrate these capabilities through comprehensive benchmarks and illustrative examples. With its unique features, DeepQuantum is intended to be a powerful platform for both AI for Quantum and Quantum for AI.
@article{arxiv.2512.18995,
title = {DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing},
author = {Jun-Jie He and Ke-Ming Hu and Yu-Ze Zhu and Guan-Ju Yan and Shu-Yi Liang and Xiang Zhao and Ding Wang and Fei-Xiang Guo and Ze-Feng Lan and Xiao-Wen Shang and Zi-Ming Yin and Xin-Yang Jiang and Lin Yang and Hao Tang and Xian-Min Jin},
journal= {arXiv preprint arXiv:2512.18995},
year = {2026}
}
Comments
31 pages, 32 figures, 3 tables. Code is available at https://github.com/TuringQ/deepquantum