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

TL;DR

This paper introduces a scalable, resource-efficient deep quantum neural network implemented on photonic hardware, utilizing Hilbert space expansion to realize nonlinear activation without ancillary qubits, enabling advanced quantum learning tasks.

Contribution

The authors propose a novel deep photonic QNN architecture that expands the Hilbert space via input replication, eliminating ancillary qubits and measurement overhead, enhancing scalability and efficiency.

Findings

Demonstrated nonlinear classification and image generation capabilities.

Achieved dimension-enhanced expressivity over existing QNNs.

Fabricated a chip with entanglement sources and high-dimensional interferometry.

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.