The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: 1st Annual Workshop on Large-Scale Quantum-Classical Computing
Authors: Jim Garrison (IBM)
Abstract: Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical algorithm which approximates the ground state of a many-body quantum system. This talk introduces a new Qiskit addon for SQD, implemented as a modern C++ template library. Closely related to the original Python-based SQD addon, this new library provides high-performance implementations of key algorithmic components: post-selection, subsampling, and configuration recovery. It is designed to integrate with the SBD eigensolver developed at RIKEN, enabling large-scale SQD calculations.This talk will demonstrate how these components, together with the Qiskit C API, enable the construction of fully compiled applications for hybrid quantum/classical workflows. As a case study, we present an open-source application that uses SQD to approximate the ground state energy of the Fe₄S₄ cluster—a molecule of interest in quantum chemistry. This example showcases Qiskit's readiness for high-performance computing environments and its growing support for compiled, scalable quantum-classical applications.
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