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

MdM Quantum meeting

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 pro-

jective 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.

Broadcast link:

https://us06web.zoom.us/j/89077864100?pwd=8jHpJwDQ9dUc5aQwZ9Z1ecmD9yajI7.1