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Feedback control systems01:26

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic

Takuya Hatomura1

  • 1NTT, Inc., Basic Research Laboratories & NTT Research Center for Theoretical Quantum Information, Kanagawa 243-0198, Japan.

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We introduce the Counterdiabaticity-Assisted Classical Algorithm for Optimization (CACAO), a novel quantum-inspired method for solving complex combinatorial optimization problems using classical spin dynamics.

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Area of Science:

  • Quantum Computing
  • Computational Optimization
  • Algorithm Development

Background:

  • Combinatorial optimization problems are computationally challenging.
  • Quantum computing offers potential solutions but faces hardware limitations.
  • Classical algorithms inspired by quantum mechanics are being explored.

Purpose of the Study:

  • To introduce a new quantum-inspired classical algorithm for combinatorial optimization.
  • To evaluate the performance of this algorithm against existing quantum algorithms.
  • To test the scalability of the algorithm on large systems.

Main Methods:

  • Development of the Counterdiabaticity-Assisted Classical Algorithm for Optimization (CACAO).
  • Utilizing classical spin dynamics and quantum Lyapunov control.
  • Comparing CACAO with quantum annealing, FALQON, and CD-FQA.
  • Testing CACAO on systems with up to 10,000 spins.

Main Results:

  • CACAO demonstrates a viable classical approach to combinatorial optimization.
  • Performance comparisons reveal CACAO's potential against quantum algorithms.
  • The algorithm shows scalability for large-scale problems.

Conclusions:

  • CACAO offers a promising quantum-inspired classical alternative for optimization.
  • The algorithm's performance on large systems warrants further investigation.
  • This approach could bridge the gap between classical and quantum optimization methods.