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    Area of Science:

    • Computational physics
    • Optimization algorithms
    • Stochastic computing

    Background:

    • Probabilistic bits (p-bits) are emerging as fundamental computational elements for simulated annealing (SA) in Ising models.
    • Existing p-bit implementations face limitations in energy efficiency and convergence speed for complex optimization tasks.

    Purpose of the Study:

    • To introduce a novel, fast-converging simulated annealing (SA) method utilizing p-bits engineered with integral stochastic computing.
    • To enhance the search capability for combinatorial optimization problems by leveraging stochastic implementations of p-bits.

    Main Methods:

    • Development of a stochastic computing-based SA algorithm that approximates p-bit functionality.
    • Comparative analysis against conventional SA and D-Wave Two quantum annealing (QA).
    • Testing on benchmark problems: Traveling Salesman Problem, Maximum Cut (MAX-CUT), and Graph Isomorphism (GI).

    Main Results:

    • The proposed stochastic computing-based SA demonstrates convergence speeds several orders of magnitude faster than conventional SA and QA.
    • The method successfully handles optimization problems with an order of magnitude larger number of spins.
    • Improved probability of finding solutions by searching near the global minimum energy.

    Conclusions:

    • Stochastic computing-based p-bits offer a significant advancement in SA for combinatorial optimization.
    • This method presents a more energy-efficient and faster alternative to existing SA and QA approaches.
    • The enhanced performance suggests broad applicability in solving large-scale optimization challenges.