Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing

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• github.com/louisanity/photonicquantumtrain
  OfficialIn paperpytorch★ 0

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 . Empirical validation on MNIST classification demonstrates that photonic QT achieves an accuracy of 95.50\% 0.84\% using 3,292 parameters ( = 10), compared to 96.89\% 0.31\% for classical baselines with 6,690 parameters. Moreover, a ten-fold compression ratio is achieved at = 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\% 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.

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