Quantum Reinforcement Learning with Quantum Photonics

Lucas Lamata

doi:10.3390/photonics8020033

Abstract

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

Highlights

Without the aim of being exhaustive, here we briefly describe some other works that have appeared in the literature on the field of quantum reinforcement learning with quantum photonics
We reviewed the field of quantum reinforcement learning with quantum photonics
Without the goal of being exhaustive, we have firstly reviewed the area of quantum reinforcement learning in general, showing that automated quantum agents can provide sometimes enhancements with respect to classical computers

Summary

Introduction

Inside artificial intelligence and machine learning, the area of reinforcement learning designs “intelligent” agents capable of interacting with their outer world, the “environment”, and adapt to it, via reward mechanisms [9], see Figure 1. These agents aim at achieving a final goal that maximizes their long-term rewards.

Quantum Reinforcement Learning

Theoretical Proposal

Implementation with Quantum Photonics

Further Developments of Quantum Reinforcement Learning with Quantum Photonics

Findings

Conclusions