Quantum reinforcement learning in the presence of thermal dissipation

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.

arXiv comment8 pages, 6 figures

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• Almost fault-tolerant quantum machine learning with drastic overhead reduction
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
• Noise-Resilient Quantum Reinforcement Learning