| Autor: |
Naoya Onizawa, Kota Katsuki, Duckgyu Shin, Warren J. Gross, Takahiro Hanyu |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE transactions on neural networks and learning systems. |
| ISSN: |
2162-2388 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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