Error-mitigated photonic quantum circuit Born machine

TL;DR

This paper introduces a photonic quantum circuit Born machine enhanced with recycling error mitigation, demonstrating improved training and performance in realistic photon loss scenarios through simulations and experiments.

Contribution

It presents a novel photonic QCBM design combined with recycling mitigation to counteract photon loss, advancing quantum generative models in photonic systems.

Findings

Recycling mitigation significantly improves QCBM training in photon loss conditions.

Numerical simulations confirm enhanced performance with error mitigation.

Experimental results validate the effectiveness of the approach on a photonic processor.

Abstract

In this article, we study quantum circuit Born machines (QCBMs) in the context of photonic quantum computing. QCBMs are a popular choice of quantum generative machine learning models, and we present a QCBM designed for linear optics. We show that a recently developed error mitigation technique called recycling mitigation greatly improves the training of QCBMs in realistic scenarios with photon loss, which is the primary source of noise in photonic systems. We demonstrate this through numerical simulations and through an experiment on a quantum photonic integrated processor. We expect our work to pave the way towards more demonstrations of error mitigation techniques tailored to photonic devices which can enhance the performance of a quantum algorithm.