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Feedback-Based Quantum Optimization.

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This study introduces a novel feedback strategy for quantum optimization. This method improves combinatorial optimization solutions using qubit measurements, bypassing classical optimization needs.

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Area of Science:

  • Quantum Computing
  • Computational Optimization
  • Quantum Information Science

Background:

  • Classical computers face limitations in solving complex combinatorial optimization problems.
  • Quantum computing offers potential advantages for tackling these computationally intensive tasks.
  • Current quantum approximate optimization algorithms often require significant classical optimization effort.

Purpose of the Study:

  • To introduce a novel feedback-based strategy for quantum optimization.
  • To enable approximate solutions for combinatorial optimization problems without classical optimization.
  • To demonstrate the efficacy of this protocol on a real quantum processor.

Main Methods:

  • A feedback-based strategy using qubit measurement results to assign quantum circuit parameters.
  • Implementation of the protocol on a superconducting quantum processor.
  • Application to the MaxCut graph-partitioning problem.
  • Numerical analyses to investigate protocol performance.

Main Results:

  • The feedback-based strategy yields monotonically improving estimates of the optimization solution with increasing circuit depth.
  • Approximate solutions are achieved without requiring classical optimization loops.
  • Successful demonstration on a superconducting quantum processor for MaxCut.

Conclusions:

  • The proposed measurement-based feedback strategy is a viable approach for quantum optimization.
  • This method offers a potential advantage over existing quantum approximate optimization algorithms by reducing reliance on classical computation.
  • The protocol shows promise for solving combinatorial optimization problems on near-term quantum hardware.