  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.
