Benchmarking a wide range of optimisers for solving the Fermi-Hubbard model using the variational quantum eigensolver

2024/11/20 by Benjamin D. M. Jones, Jones, Benjamin D. M., Lana Mineh +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2411.13742

openalex publication_date 2024/11/20 · openalex created_date 2024/11/24 · openalex updated_date 2026/08/01

Abstract

Data used in the paper "Benchmarking a wide range of optimisers for solving the Fermi-Hubbard model using the variational quantum eigensolver" by Benjamin D.M. Jones, Lana Mineh, and Ashley Montanaro, available here: https://arxiv.org/abs/2411.13742. See the README.md file for details on how the data is organised, and data.zip for the full data (~37GB when uncompressed).

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