Selected Basis Diagonalization (SBD) plays a central role in Sample-based Quantum Diagonalization (SQD), where iterative diagonalization of the Hamiltonian in selected configuration subspaces forms the dominant classical workload. We present a GPU-accelerated implementation of SBD using the Thrust library. By restructuring key components -- including configuration processing, excitation generation, and matrix-vector operations -- around fine-grained data-parallel primitives and flattened GPU-friendly data layouts, the proposed approach efficiently exploits modern GPU architectures. In our experiments, the Thrust-based SBD achieves up to ∼40× speedup over CPU execution and substantially reduces the total runtime of SQD iterations. These results demonstrate that GPU-native parallel primitives provide a simple, portable, and high-performance foundation for accelerating SQD-based quantum-classical workflows.
@article{arxiv.2601.16637,
title = {GPU-Accelerated Selected Basis Diagonalization with Thrust for SQD-based Algorithms},
author = {Jun Doi and Tomonori Shirakawa and Yukio Kawashima and Seiji Yunoki and Hiroshi Horii},
journal= {arXiv preprint arXiv:2601.16637},
year = {2026}
}