Optical quantum walk platforms for interpretable quantum machine learning

This repository provides code and data for the paper Optical quantum walk platforms for interpretable quantum machine learning by Xing Tang,Jiameng Tian, Tian Chen, and Xiangdong Zhang We deploy Quantum Projective Simulation with optical QW networks in two typical reinforcement learning scenarios.This repository consists of two separate packages:Scenario 1: A Jupyter notebook simulating a QW network and using it to realize QPS. The considered task is the scenario investigated in B. Eva, K. Ried, T. Mueller, and H. J. Briegel, How a minimal learning agent can infer the existence of unobserved variables in a complex environment, Minds & Machines, pp. 1-35, 2022, online first, DOI = 10.1007/s11023-022-09619-5. The code refers to the project: https://github.com/MariusKrumm/PhotonicsPS.gitScenario 2: A QPS agent implemented with an Optical QW network to solve the gridworld problem, with results comparable to those of the classical PS agent. The code refers to the project: https://github.com/qic-ibk/projectivesimulation.git