Hardware Robustness of Sample-Based Quantum Diagonalization

• URL: http://arxiv.org/abs/2607.18196v1
• Date: Mon, 20 Jul 2026 17:36:08 GMT
• Title: Hardware Robustness of Sample-Based Quantum Diagonalization
• Abstract summary: Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples.<n>We analyze SQD robustness on IBM Heron hardware across these dimensions.
• Score: 17.52301775749581
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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