Hardware Robustness of Sample-Based Quantum Diagonalization

2026-07-20

AI Summary

This study addresses the lack of systematic evaluation of the robustness of Sample-Based Quantum Diagonalization (SQD) under realistic hardware constraints, particularly regarding sampling budgets, qubit layouts, error mitigation strategies, and the choice of CCSD initial amplitudes. Conducted on an IBM Heron processor, this work presents the first comprehensive quantification of SQD’s robustness boundaries by replacing conventional variational optimization with a self-consistent recovery loop and empirically exploring multiple qubit mappings, noise mitigation techniques, and sampling regimes. The results demonstrate that SQD exhibits remarkable resilience to perturbations in CCSD amplitudes—even complete zeroing induces only minor energy deviations. Although initial performance varies significantly across layouts and noise configurations, convergence is rapidly achieved within a few iterations. Moreover, moderate sampling budgets suffice to reach accuracy saturation, with higher budgets yielding marginally degraded precision.

Abstract

Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Although SQD is considered robust to noisy samples and imperfect classical inputs, its robustness across practical deployment choices has not been systematically analyzed. As a result, shot budgets, qubit layouts, noise mitigation strategies, and the coupled-cluster singles and doubles (CCSD) amplitudes that initialize the ansatz are often chosen without clear empirical guidance. We analyze SQD robustness on IBM Heron hardware across these dimensions. Structured CCSD-amplitude perturbations, including complete zeroing, produce only modest energy shifts from the clean baseline. Differences across layouts and noise-mitigation settings are large in the first recovery iteration but narrow within a few iterations. Accuracy saturates at moderate shot budgets, while very large budgets slightly worsen recovered energies, likely because working-set selection limits the value of additional samples. These results identify where SQD provides genuine deployment robustness and where its limits remain.

Problem

• Hardware Robustness
• Quantum Diagonalization
• Shot Budget
• Qubit Layout
• Noise Mitigation

Innovation

• Sample-Based Quantum Diagonalization
• hardware robustness
• noise mitigation
• CCSD initialization
• self-consistent recovery