Ising Hamiltonian minimization: Gain-based computing with manifold reduction of soft-spins vs quantum annealing

Cummins, James S., Salman, Hayder and Berloff, Natalia G. (2024) Ising Hamiltonian minimization: Gain-based computing with manifold reduction of soft-spins vs quantum annealing. Physical Review Research. (In Press)

PDF (Space_structure_of_physical_optimisers-accepted) - Accepted Version Restricted to Repository staff only until 31 December 2099. Request a copy

Abstract

We investigate the minimization of Ising Hamiltonians, comparing the performance of gain-based computing paradigms based on the dynamics of semi-classical soft-spin models with quantum an- nealing. We systematically analyze how the energy landscape for the circulant couplings of a Mo ̈bius graph evolves with increased annealing parameters. Our findings indicate that these semi-classical models face challenges due to a widening dimensionality landscape. To counteract this issue, we introduce the ‘manifold reduction’ method, which restricts the soft-spin amplitudes to a defined phase space region. Concurrently, quantum annealing demonstrates a natural capability to navigate the Ising Hamiltonian’s energy landscape due to its operation within the comprehensive Hilbert space. Our study indicates that physics-inspired or physics-enhanced optimizers will likely benefit from combining classical and quantum annealing techniques.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Engineering, Mathematics and Physics
UEA Research Groups:	Faculty of Science > Research Groups > Centre for Photonics and Quantum Science Faculty of Science > Research Groups > Fluids & Structures Faculty of Science > Research Groups > Quantum Matter
Depositing User:	LivePure Connector
Date Deposited:	07 Jan 2025 02:17
Last Modified:	07 Jan 2025 02:17
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98104
DOI:

Actions (login required)

View Item