Simulated bifurcation assisted by thermal fluctuation
arXiv:2203.08361 · doi:10.1038/s42005-022-00929-9
Abstract
Various kinds of Ising machines based on unconventional computing have recently been developed for practically important combinatorial optimization. Among them, the machines implementing a heuristic algorithm called simulated bifurcation have achieved high performance, where Hamiltonian dynamics are simulated by massively parallel processing. To further improve the performance of simulated bifurcation, here we introduce thermal fluctuation to its dynamics relying on the Nosé-Hoover method, which has been used to simulate Hamiltonian dynamics at finite temperatures. We find that a heating process in the Nosé-Hoover method can assist simulated bifurcation to escape from local minima of the Ising problem, and hence lead to improved performance. We thus propose heated simulated bifurcation and demonstrate its performance improvement by numerically solving instances of the Ising problem with up to 2000 spin variables and all-to-all connectivity. Proposed heated simulated bifurcation is expected to be accelerated by parallel processing.
6 pages, 4 figures
References in corpus (6)
- Network of Time-Multiplexed Optical Parametric Oscillators as a Coherent Ising Machine
- Large-scale photonic Ising machine by spatial light modulation
- Intrinsic optimization using stochastic nanomagnets
- Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization
- Chaos in coupled Kerr-nonlinear parametric oscillators
- Quantum Correlations in the Kerr Ising Model