Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing

--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 χ . Empirical validation on MNIST classification demonstrates that photonic QT achieves an accuracy of 95 .50% Moreover, a ten-fold compression ratio is achieved at χ = 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 . Photonic quantum computing room-temperature operation, inherent scalability through spatial mode multiplexing, and HPC-integrated architecture establish a practical pathway for distributed quantum machine learning, combining the ex-pressivity of photonic Hilbert spaces with the deployability of classical neural networks. Quantum-centric supercomputing represents a transforma-tive paradigm integrating classical high-performance computing (HPC) architectures with distributed quantum resources to overcome fundamental limitations of isolated quantum systems [1], [2].