A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians
quant-ph
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 10 pages, 1 figure
Code: https://github.com/UTokyo-ICEPP/rqutils
Project page: https://nvidia.github.io/cudaquantum/latest/applications/python/skqd.html
License: http://creativecommons.org/licenses/by/4.0/
The gist: A scalable ground-state solver to be used in sample-based quantum diagonalization (SQD) is presented.
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.
Sources
- GPU-Accelerated Selected Basis Diagonalization with Thrust for SQD-based Algorithms
- Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale
- Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
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