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Optimal quantum reservoir computing for the noisy intermediate-scale quantum era.

L Domingo1,2,3, G Carlo4, F Borondo1,2

  • 1Instituto de Ciencias Matemáticas, Campus de Cantoblanco, Nicolás Cabrera, 13-15, 28049 Madrid, Spain.

Physical Review. E
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Summary
This summary is machine-generated.

This study introduces a method for selecting optimal quantum reservoirs for quantum reservoir computing. These reservoirs use fewer, simpler gates, achieving better results on current noisy intermediate-scale quantum (NISQ) devices.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Quantum Reservoir Computing

Background:

  • Universal fault-tolerant quantum computers are distant, increasing interest in Noisy Intermediate-Scale Quantum (NISQ) computation.
  • Quantum reservoir computing is a machine learning approach suitable for NISQ devices due to its training simplicity.

Purpose of the Study:

  • To develop a criterion for selecting optimal quantum reservoirs for quantum reservoir computing.
  • To identify reservoirs that require minimal and simple quantum gates.

Main Methods:

  • Proposed a criterion for selecting quantum reservoirs.
  • Evaluated reservoir performance based on gate count and complexity.

Main Results:

  • Identified optimal quantum reservoirs requiring few and simple gates.
  • Demonstrated superior performance compared to commonly used models with significantly fewer gates.
  • Provided insights into the theoretical gap between quantum reservoir computing and quantum state complexity.

Conclusions:

  • The proposed criterion enables efficient selection of quantum reservoirs for NISQ computation.
  • Optimized quantum reservoirs offer a practical approach to leveraging current quantum hardware for machine learning tasks.