Hybrid quantum-classical photonic neural networks.

Abstract

Neuromorphic (brain-inspired) photonics accelerates AI¹ with high-speed, energy-efficient solutions for RF communication², image processing^3,4, and fast matrix multiplication^5,6. However, integrated neuromorphic photonic hardware faces size constraints that limit network complexity. Recent advances in photonic quantum hardware⁷ and performant trainable quantum circuits⁸ offer a path to more scalable photonic neural networks. Here, we show that a combination of classical network layers with trainable continuous variable quantum circuits yields hybrid networks with improved trainability and accuracy. On a classification task, these hybrid networks match the performance of classical networks nearly twice their size. These performance benefits remain even when evaluated at state-of-the-art bit precisions for classical and quantum hardware. Finally, we outline available hardware and a roadmap to hybrid architectures. These hybrid quantum-classical networks demonstrate a unique route to enhance the computational capacity of integrated photonic neural networks without increasing the network size.