Quantum reinforcement learning in the presence of thermal dissipation

TL;DR

This paper investigates how thermal dissipation affects quantum reinforcement learning, showing that low temperatures do not significantly impair performance and may even be advantageous, facilitating practical quantum agents.

Contribution

It adapts a nondissipative quantum reinforcement learning protocol to include thermal dissipation and analyzes its effects through analytical and numerical methods.

Findings

Dissipation does not significantly degrade performance at low temperatures

Thermal dissipation can sometimes improve quantum reinforcement learning

Supports development of quantum agents for realistic environments

Abstract

A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulations are carried out obtaining evidence that dissipation do not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, being in some cases even beneficial. Quantum reinforcement learning under realistic experimental conditions of thermal dissipation opens an avenue for the realization of quantum agents able to interact with a changing environment, and adapt to it, with plausible many applications inside quantum technologies and machine learning.