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Quantum reservoir computing (QRC) performance is enhanced at the "edge of many-body quantum chaos." This boundary, near the Thouless time and integrable-to-chaotic transition, guides QRC design.

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Area of Science:

  • Quantum computing
  • Machine learning
  • Complex systems

Background:

  • Reservoir computing (RC) utilizes dynamical systems for computation.
  • Quantum reservoir computing (QRC) extends RC to quantum systems.
  • Optimal classical RC performance occurs at the
  • edge of chaos
  • a balance between order and chaos.

Purpose of the Study:

  • To identify the quantum many-body counterpart of the
  • edge of chaos
  • in QRC.
  • To investigate performance enhancements in QRC systems.
  • To establish design guidelines for QRC based on quantum chaos.

Main Methods:

  • Implemented QRC on the Sachdev-Ye-Kitaev (SYK) model.
  • Analyzed system dynamics near critical boundaries.
  • Investigated performance metrics related to temporal and parametric edges.

Main Results:

  • Identified two critical
  • edges
  • enhancing QRC performance.
  • Performance boosts were observed near the Thouless time (temporal boundary).
  • Enhancements also occurred near the integrable-to-chaotic transition (parametric boundary).

Conclusions:

  • The
  • edge of many-body quantum chaos
  • is crucial for QRC.
  • This boundary offers a design principle for optimizing QRC.
  • QRC performance is significantly influenced by quantum chaotic dynamics.