• 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={M. L. Olivera-Atencio and Lucas Lamata and Manuel Morillo and Jes{\'u}s Casado-Pascual},
  year={2022}
}
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… 

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References

SHOWING 1-10 OF 12 REFERENCES

Quantum Computing and Quantum Information

A quantum algorithm that has a query complexity O(n(logn) √ k) is proposed, which shows speed-up comparing with the deterministic algorithm (radix sort) that requires Ω((n+ d)k) queries, where d is a size of the alphabet.

Mach

  • Learn.: Sci. Technol. 1, 015002
  • 2020

Science 376

  • 1182
  • 2022

Phys

  • Rev. Lett. 103, 150502
  • 2009

Nature 591

  • 229
  • 2021

Nature Phot

  • 16, 318
  • 2022

Nature 567

  • 209
  • 2019

Phys

  • Rev. Lett. 113, 130503
  • 2014

Nature 549

  • 195
  • 2017

Mach

  • Learn. Sci. Technol. 1, 033002
  • 2020