Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion

• URL: http://arxiv.org/abs/2605.06397v1
• Date: Thu, 07 May 2026 15:10:07 GMT
• Title: Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion
• Abstract summary: Quantum neural networks (QNNs) implemented on integrated photonic platforms offer exceptional computational power enhancements.<n>A critical challenge is implementing the fundamental non-unitary and nonlinear activation function of QNNs within a linear quantum photonic system.<n>Here, we propose a novel deep photonic QNN with an expanded computational Hilbert space via input replication and mode expansion.<n>This approach eliminates the need for physical ancillary qubits, measurement-induced qubit consumption and the measurement device burden.
• Score: 14.87228229418423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing computational demands of classical neural networks have intensified the search for energy-efficient and powerful computational alternatives. Quantum neural networks (QNNs) implemented on integrated photonic platforms offer a compelling avenue, offering exceptional computational power enhancements, with inherent programmability and scalability of integrated architectures. A critical challenge, however, is implementing the fundamental non-unitary and nonlinear activation function of QNNs within a linear quantum photonic system. Existing strategies, such as the adding ancillary qubits and measurement-based feedback or forward are constrained by high qubit resource costs, overhead devices, and poor cascadability. Here, we propose a novel deep photonic QNN with an expanded computational Hilbert space via input replication and mode expansion, which enables the realization of effective non-unitary and nonlinear activation on a linear programmable quantum photonic chip. This approach eliminates the need for physical ancillary qubits, measurement-induced qubit consumption and the measurement device burden, thereby significantly reduce resource costs. The fabricated chip integrates four high-quality entanglement sources and a programmable high-dimensional interferometric network, enabling a two-hidden-layer QNN that exhibits dimension-enhanced expressivity over the existing QNN architectures. We demonstrate its capabilities across diverse tasks, including nonlinear classification, image generation, and quantum Gibbs state preparation. This work establishes a scalable and efficient architecture toward practical quantum deep learning systems capable of tackling problems beyond the reach of classical computation.

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