Work

A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.13907

Author: Iiyama, Yutaro

Institutions: International Center for Elementary Particle Physics, The University of Tokyo

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

OpenAlex ID: W7221552608

Cites: 8

Cited by:

Related to: 10