Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing

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Abstract

— Probabilistic bits (p-bits) have recently been presented as a spin (basic computing element) for the simulated annealing (SA) of Ising models. In this brief, we introduce fast-converging SA based on p-bits designed using integral stochastic computing. The stochastic implementation approximates a p-bit function, which can search for a solution to a combinatorial optimization problem at lower energy than conventional p-bits. Searching around the global minimum energy can increase the probability of finding a solution. The proposed stochastic computing-based SA method is compared with conventional SA and quantum annealing (QA) with a D-Wave Two quantum annealer on the traveling salesman, maximum cut (MAX-CUT), and graph isomorphism (GI) problems. The proposed method achieves a convergence speed a few orders of magnitude faster while dealing with an order of magnitude larger number of spins than the other methods.

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APA

Onizawa, N., Katsuki, K., Shin, D., Gross, W. J., & Hanyu, T. (2023). Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing. IEEE Transactions on Neural Networks and Learning Systems, 34(12), 10999–11005. https://doi.org/10.1109/TNNLS.2022.3159713

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