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 $2^{30}$-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.

arXiv:2610.13907 [quant-ph]

(or arXiv:2610.13907v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.13907 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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