Quantum optical neural networks with programmable nonlinearities

TL;DR

This paper introduces a novel quantum optical neural network architecture that utilizes programmable nonlinearities, specifically Kerr-like effects, to improve efficiency and reduce complexity compared to traditional linear optical approaches.

Contribution

It demonstrates the use of programmable nonlinearities in quantum optical circuits, significantly reducing parameters and circuit depth for high-fidelity quantum neural networks.

Findings

Programmable nonlinearities enhance quantum neural network efficiency.

Reduced circuit depth and parameters compared to linear optical methods.

Potential for improved quantum circuit scalability.

Abstract

Parametrized quantum circuits are essential components of variational quantum algorithms. Until now, optical implementations of these circuits have relied solely on adjustable linear optical units. In this study, we demonstrate that using programmable nonlinearities, rather than linear optics, offers a more efficient method for constructing quantum optical circuits -- especially quantum neural networks. This approach significantly reduces the number of adjustable parameters and the circuit depth needed to achieve high-fidelity operation. Specifically, we explored a quantum optical neural network (QONN) architecture composed of meshes of two-mode interferometers programmable by adjustable Kerr-like nonlinearities. We assessed the capabilities of our quantum optical neural network architecture and compared its performance to previously studied architectures that use multimode linear optics units. Additionally, we suggest future research directions for improving programmable quantum optical circuits.