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This study benchmarks physics-inspired optimization algorithms like coherent Ising machines against simulated annealing for solving hard combinatorial problems. These novel methods show promise for tackling complex optimization challenges.

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Area of Science:

  • Computational physics
  • Optimization algorithms
  • Machine learning

Background:

  • Combinatorial optimization problems are widespread in industry but computationally challenging.
  • Mapping these problems to Ising models has driven the development of specialized solvers.
  • Recent quantum and classical computing advances offer new approaches to optimization.

Purpose of the Study:

  • To benchmark various physics-inspired optimization algorithms.
  • To compare their performance against simulated annealing on Ising problems.
  • To investigate the impact of numerical methods and network structures on performance.

Main Methods:

  • Benchmarking coherent Ising machines, gain-dissipative algorithms, simulated bifurcation machines, and Hopfield networks.
  • Utilizing random Ising problems with planted solutions for evaluation.
  • Comparing performance against simulated annealing using a unified software stack.
  • Analyzing the effects of different numerical integration techniques and graph connectivities.

Main Results:

  • Performance evaluation of stochastic driven nonlinear dynamical systems against simulated annealing.
  • Identification of factors influencing algorithm efficiency, such as numerical integration and graph topology.
  • Comparative analysis of novel optimization paradigms for Ising-type problems.

Conclusions:

  • Physics-inspired algorithms offer alternative approaches to solving hard optimization problems.
  • Understanding the influence of numerical and structural parameters is crucial for algorithm selection.
  • This work provides a valuable overview of emerging optimization paradigms.