Hyperscaling in the Coherent Hyperspin Machine

Marcello Calvanese Strinati, Claudio Conti

Research output: Contribution to journalArticlepeer-review

Abstract

Classical and quantum systems are used to simulate the Ising Hamiltonian, an essential component in large-scale optimization and machine learning. However, as the system size increases, devices like quantum annealers and coherent Ising machines face an exponential drop in their success rate. Here, we introduce a novel approach involving high-dimensional embeddings of the Ising Hamiltonian and a technique called "dimensional annealing"to counteract the decrease in performance. This approach leads to an exponential improvement in the success rate and other performance metrics, slowing down the decline in performance as the system size grows. A thorough examination of convergence dynamics in high-performance computing validates the new methodology. Additionally, we suggest practical implementations using technologies like coherent Ising machines, all-optical systems, and hybrid digital systems. The proposed hyperscaling heuristics can also be applied to other quantum or classical Ising devices by adjusting parameters such as nonlinear gain, loss, and nonlocal couplings.

Original languageEnglish
Article number017301
JournalPhysical Review Letters
Volume132
Issue number1
DOIs
StatePublished - 5 Jan 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 American Physical Society.

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