Quantum photonic neural networks in time

TL;DR

This paper introduces a novel time-bin-encoded quantum photonic neural network architecture, demonstrating its training for quantum gates and Bell-state analysis with high fidelity, robustness to imperfections, and potential for scalable quantum information processing.

Contribution

The paper presents the first framework for a time-encoded quantum photonic neural network, including a timing algorithm and methods to include realistic imperfections.

Findings

QPNN can realize a CNOT gate with ideal Kerr nonlinearity.

QPNN trained as a Bell-state analyzer achieves 0.96 fidelity with realistic nonlinearity.

Time gating improves fidelity to over 0.99 while maintaining high efficiency.

Abstract

We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN): a reconfigurable nonlinear photonic circuit inspired by the brain and trained to process quantum information. Unlike the typical spatially-encoded QPNN, time-encoded networks require the same number of photonic elements (e.g. phase shifters or switches) regardless of their size or depth. Here, we present a model of such a network and show how to include imperfections such as losses, routing errors and most notably distinguishable photons. As an example, we train the QPNN to realize a controlled-NOT gate, based on a hypothetical ideal Kerr nonlinearity. We then extend our model to a realistic two-photon nonlinearity due to scattering from a single, semiconductor quantum dot coupled to a photonic waveguide. We show that, using this realistic nonlinearity, the QPNN can be trained to act as a Bell-state analyzer which operates with a fidelity of 0.96 and at a rate only limited by losses. We further show that time gating can raise this fidelity to over 0.99, while still maintaining an efficiency exceeding 0.9. Overall, this work lays a framework for the first QPNN encoded in time, and provides a clear path to the scaling of these networks.