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Basic protocols in quantum reinforcement learning with superconducting circuits.

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  • 1Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080, Bilbao, Spain. lucas.lamata@gmail.com.

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We propose quantum reinforcement learning using superconducting circuits with feedback control. This enables quantum devices to learn from their environment, advancing quantum artificial intelligence and computation.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence

Background:

  • Superconducting circuits have enabled quantum protocols with feedback loops.
  • Quantum artificial intelligence and machine learning are emerging fields in quantum technologies.

Purpose of the Study:

  • To propose and analyze basic protocols for quantum reinforcement learning using superconducting circuits.
  • To demonstrate proof-of-principle experiments for quantum reinforcement learning.

Main Methods:

  • Implementation of quantum reinforcement learning protocols on superconducting circuits.
  • Utilizing feedback-loop control for quantum device learning.
  • Analysis of experimental feasibility in the presence of imperfections.

Main Results:

  • Demonstration of basic quantum reinforcement learning protocols.
  • Analysis of superconducting circuit performance under realistic conditions.
  • Identification of pathways for enhanced quantum control and computation.

Conclusions:

  • Superconducting circuits with feedback control are viable for quantum reinforcement learning.
  • This research paves the way for advanced quantum artificial intelligence and computation.
  • The findings support the development of self-improving quantum devices.