GPU-Accelerated Selected Basis Diagonalization with Thrust for SQD-based Algorithms

TL;DR

This paper introduces a GPU-accelerated implementation of Selected Basis Diagonalization using Thrust, significantly speeding up quantum diagonalization tasks in SQD algorithms by leveraging data-parallel primitives.

Contribution

The paper presents a novel GPU-based approach for SBD in SQD algorithms, optimizing key components with Thrust to achieve high performance and portability.

Findings

Achieves up to 40x speedup over CPU implementations

Reduces total runtime of SQD iterations substantially

Demonstrates effectiveness of GPU-native primitives for quantum workflows

Abstract

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 $\sim$40$\times$ 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.