• Corpus ID: 251564699

Quantum reinforcement learning in the presence of thermal dissipation

@inproceedings{OliveraAtencio2022QuantumRL,
  title={Quantum reinforcement learning in the presence of thermal dissipation},
  author={Mar{\'i}a Laura Olivera-Atencio and Lucas Lamata and Manuel Morillo and Jes{\'u}s Casado-Pascual},
  year={2022}
}

• M. L. Olivera-Atencio, L. Lamata, +1 author J. Casado-Pascual
• Published 12 August 2022
• Physics

A quantum reinforcement learning protocol in the presence of thermal dissipation is introduced and analyzed. Analytical calculations as well as numerical simulations are carried out, obtaining evi-dence that decoherence and dissipation do not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, being in some cases even benefi-cial. Quantum reinforcement learning under realistic experimental conditions of decoherence and 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.