Demonstration of quantum projective simulation on a single-photon-based quantum computer

TL;DR

This paper demonstrates a quantum reinforcement learning algorithm using a single-photon quantum computer, showcasing its potential advantages over classical methods in decision-making tasks.

Contribution

It presents the first implementation of a quantum projective simulation algorithm on a single-photon quantum computer, bridging theoretical concepts with experimental realization.

Findings

Quantum agent outperforms classical in test tasks

Successful implementation of quantum walks on a photonic mesh

Proof of concept for quantum reinforcement learning hardware

Abstract

Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices. A novel variational approach to reinforcement learning has been recently proposed, incorporating linear-optical interferometers and a classical learning model known as projective simulation (PS). PS is a decision-making tool for reinforcement learning and can be classically represented as a random walk on a graph that describes the agent's memory. In its optical quantum version, this approach utilizes quantum walks of single photons on a mesh of tunable beamsplitters and phase shifters to select actions. In this work, we present the implementation of this algorithm on Ascella, a single-photon-based quantum computer from Quandela. The focus is drawn on solving a test bed task to showcase the potential of the quantum agent with respect to the classical agent.