2025/04/16 by Seyed Ehsan Ghasempouri, Gerhard W. Dueck, Ghasempouri, Seyed Ehsan +3 · 1 voice
Computer Science · Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Machine Learning in Materials Science #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum many-body systems #cond-mat.dis-nn #physics.chem-ph #quant-ph
paper · pdf · doi:10.48550/arxiv.2504.12096
openalex publication_date 2025/04/16 · arxiv published 2025/04/16 · arxiv updated 2025/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Finding the ground state of spin glasses is a challenging problem with broad implications. Many hard optimization problems, including NP-complete problems, can be mapped, for instance, to the Ising spin glass model. We present a graph-based approach that allows for accurate state initialization of a frustrated triangular spin-lattice with up to 20 sites that stays away from barren plateaus. To optimize circuit efficiency and trainability, we employ a clustering strategy that organizes qubits into distinct groups based on the maximum cut technique, which divides the lattice into two subsets maximally disconnected. We provide evidence that this Max-Cut-based lattice division offers a robust framework for optimizing circuit design and effectively modeling frustrated systems at polynomial cost. All simulations are performed within the variational quantum eigensolver (VQE) formalism, the current paradigm for noisy intermediate-scale quantum (NISQ), but can be extended beyond. Our results underscore the potential of hybrid quantum-classical methods in addressing complex optimization problems.