{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/distributed-quantum-neural-networks-on","title":"Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing","arxiv_id":"2505.08474","date":"2025-05-13","proceeding":null,"authors":["Kuan-Cheng Chen","Chen-Yu Liu","Yu Shang","Felix Burt","Kin K. Leung"],"abstract":"We introduce a distributed quantum-classical framework that synergizes photonic quantum neural networks (QNNs) with matrix-product-state (MPS) mapping to achieve parameter-efficient training of classical neural networks. By leveraging universal linear-optical decompositions of $M$-mode interferometers and photon-counting measurement statistics, our architecture generates neural parameters through a hybrid quantum-classical workflow: photonic QNNs with $M(M+1)/2$ trainable parameters produce high-dimensional probability distributions that are mapped to classical network weights via an MPS model with bond dimension $\\chi$. Empirical validation on MNIST classification demonstrates that photonic QT achieves an accuracy of $95.50\\% \\pm 0.84\\%$ using 3,292 parameters ($\\chi = 10$), compared to $96.89\\% \\pm 0.31\\%$ for classical baselines with 6,690 parameters. Moreover, a ten-fold compression ratio is achieved at $\\chi = 4$, with a relative accuracy loss of less than $3\\%$. The framework outperforms classical compression techniques (weight sharing/pruning) by 6--12\\% absolute accuracy while eliminating quantum hardware requirements during inference through classical deployment of compressed parameters. Simulations incorporating realistic photonic noise demonstrate the framework's robustness to near-term hardware imperfections. Ablation studies confirm quantum necessity: replacing photonic QNNs with random inputs collapses accuracy to chance level ($10.0\\% \\pm 0.5\\%$). Photonic quantum computing's room-temperature operation, inherent scalability through spatial-mode multiplexing, and HPC-integrated architecture establish a practical pathway for distributed quantum machine learning, combining the expressivity of photonic Hilbert spaces with the deployability of classical neural networks.","url_abs":"https://arxiv.org/abs/2505.08474v1","url_pdf":"https://arxiv.org/pdf/2505.08474v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"distributed-quantum-neural-networks-on","repo_url":"https://github.com/louisanity/photonicquantumtrain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"quantum-machine-learning","task_name":"Quantum Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}