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Optimizing a quantum reservoir computer for time series prediction.

Aki Kutvonen1,2, Keisuke Fujii3, Takahiro Sagawa4

  • 1Department of Applied Physics and Quantum-Phase Electronics Center (QPEC), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. aki.kutvonen@gmail.com.

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This study explores quantum reservoir computing (QRC) for temporal machine learning. Optimizing inter-spin interactions and timescales enhances QRC

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Information Processing

Background:

  • Quantum computing and neural networks offer advanced information processing capabilities.
  • Quantum reservoir computing (QRC) leverages quantum dynamics for efficient temporal machine learning tasks.
  • Applications include speech recognition, time series prediction, and natural language processing.

Purpose of the Study:

  • Investigate the memory capacity and accuracy of a QRC based on the transverse field Ising model.
  • Analyze the impact of inter-spin interactions and computing timescales on QRC performance.
  • Correlate computational capabilities with physical properties like out-of-time-ordered correlators.

Main Methods:

  • Studied a quantum reservoir computer (QRC) model.
  • Varied inter-spin interactions and computing timescales.
  • Analyzed memory capacity and accuracy.
  • Investigated out-of-time-ordered correlators (OTOCs).

Main Results:

  • Varying inter-spin interactions generally improves memory capacity.
  • Engineered interactions significantly enhance QRC capacity.
  • An optimal timescale maximizes memory capacity.
  • Faster decay of out-of-time-ordered correlators correlates with more accurate memory.

Conclusions:

  • Inter-spin interactions and timescales are critical for QRC performance.
  • QRC shows promise for temporal machine learning tasks.
  • Out-of-time-ordered correlators provide insights into QRC memory accuracy.
  • Demonstrated QRC for stock value prediction.