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A new multistart algorithm enhances low-energy samplers for optimization problems by iteratively fixing variables. This improves performance and scaling for methods like quantum annealing and parallel tempering on hard problems.

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Area of Science:

  • Computational Physics
  • Optimization Algorithms
  • Quantum Computing

Background:

  • Low-energy samplers are crucial for solving complex optimization problems.
  • Existing samplers face challenges with performance and scaling on hard problem instances.

Purpose of the Study:

  • To introduce and validate a general-purpose, multistart algorithm for enhancing low-energy samplers.
  • To demonstrate the algorithm's effectiveness across various heuristic solvers and problem classes.

Main Methods:

  • The algorithm iteratively fixes variables with high probability of being optimal, simplifying the problem.
  • It is applied to simulated annealing, quantum Monte Carlo, parallel tempering, and quantum annealers.
  • The method is parallelizable and applicable to any heuristic solver run multiple times.

Main Results:

  • Substantial improvements in success metrics and scaling were observed for all tested methods.
  • Quantum annealer scaling improved significantly for native Chimera graph problems.
  • Parallel tempering solved 8000-variable 3D spin glass problems, a feat unachievable without the algorithm.

Conclusions:

  • The multistart algorithm offers a general and effective approach to boost the performance of low-energy samplers.
  • It significantly enhances the capabilities of quantum annealers and other heuristic solvers for hard optimization tasks.
  • This method shows promise for tackling larger and more complex scientific and industrial optimization challenges.