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Multiqubit and multilevel quantum reinforcement learning with quantum technologies.

F A Cárdenas-López1,2, L Lamata3, J C Retamal1,2

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Summary
This summary is machine-generated.

We present a new protocol for quantum reinforcement learning using quantum technologies. This method does not require coherent feedback, making it adaptable for various quantum systems like trapped ions and superconducting circuits.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Quantum Control

Background:

  • Quantum reinforcement learning (QRL) is an emerging field combining quantum computation with reinforcement learning.
  • Existing QRL protocols often rely on coherent feedback, limiting their applicability to specific quantum hardware.

Purpose of the Study:

  • To propose a novel protocol for quantum reinforcement learning that overcomes the limitations of existing methods.
  • To enable the implementation of QRL across a broader range of quantum systems.

Main Methods:

  • The proposed protocol utilizes a quantum agent, environment, and a connecting register.
  • It accommodates diverse scenarios including multi-qubit and multi-level systems, and open-system dynamics.
  • Coherent feedback during the learning process is not a requirement.

Main Results:

  • The protocol's flexibility allows for implementation in various quantum platforms.
  • Potential applications include trapped ions and superconducting circuits.
  • The framework supports complex interactions within quantum systems.

Conclusions:

  • The developed protocol offers a more accessible approach to quantum reinforcement learning.
  • This advancement is expected to drive progress in quantum control and machine learning.
  • Wider adoption of QRL is anticipated due to reduced hardware constraints.