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This study introduces a new heuristic inspired by quantum annealing for solving complex discrete optimization problems. It offers a scalable method leveraging quantum effects, potentially improving upon existing solutions for large-scale applications.

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Area of Science:

  • Computational Physics
  • Quantum Computing
  • Operations Research

Background:

  • Discrete optimization problems are widespread but computationally challenging.
  • Classical physics-inspired heuristics are common, but quantum annealing offers a new approach.
  • Existing quantum annealing hardware is available on analog and digital devices.

Purpose of the Study:

  • To develop a novel heuristic inspired by quantum annealing.
  • To utilize Generalized Coherent States as a variational Ansatz for quantum state representation.
  • To enable efficient computation of energy and gradients for large-scale optimization.

Main Methods:

  • Developed a quantum annealing-inspired heuristic.
  • Employed Generalized Coherent States as a parameterized variational Ansatz.
  • Analyzed energy and gradient computation with polynomial complexity.
  • Benchmarked on the 3D Edwards-Anderson model.

Main Results:

  • The heuristic allows analytical computation of energy and gradients with low polynomial complexity.
  • The method scales to problems with thousands of spins.
  • Generalized Coherent States capture essential entanglement properties.
  • Performance was compared against other popular heuristics on the 3D Edwards-Anderson model.

Conclusions:

  • The proposed heuristic provides a scalable approach to harness quantum effects for discrete optimization.
  • This method has the potential to complement or enhance conventional optimization techniques.
  • It offers a promising avenue for tackling large-scale, complex optimization challenges.