PreprintCC BY 4.0arXiv
A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians
Abstract
A scalable ground-state solver to be used in sample-based quantum diagonalization (SQD) is presented. The solver is built on JAX API, and is intended to be run on GPU platforms and possibly many-GPU high-performance computing facilities. It is capable of subspace diagonalization in up to 230-dimensional subspace, with possible upgrades to enable even larger-scale operations. The two main components, a general-purpose eigensolver and an SQD-specific matrix-vector operation function, are described in detail. Efficiency of the eigensolver with respect to the GPU memory is demonstrated using a simple physics model. Furthermore, good strong scaling of the eigensolver in many-GPU environment is observed.
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BibTeX
@misc{iiyama2026scalable,
title = {{A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians}},
author = {Yutaro Iiyama},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13907},
eprint = {2610.13907},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13907},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians
AU - Yutaro Iiyama
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13907
UR - https://arxiv.org/abs/2610.13907
AB - A scalable ground-state solver to be used in sample-based quantum diagonalization (SQD) is presented. The solver is built on JAX API, and is intended to be run on GPU platforms and possibly many-GPU high-performance computing facilities. It is capable of subspace diagonalization in up to 2^30-dimensional subspace, with possible upgrades to enable even larger-scale operations. The two main components, a general-purpose eigensolver and an SQD-specific matrix-vector operation function, are described in detail. Efficiency of the eigensolver with respect to the GPU memory is demonstrated using a simple physics model. Furthermore, good strong scaling of the eigensolver in many-GPU environment is observed.
ER -