Hardware Robustness of Sample-Based Quantum Diagonalization
This work provides empirical guidance for practitioners deploying SQD on quantum hardware, identifying where robustness holds and where limitations exist.
The paper systematically analyzes the robustness of Sample-based Quantum Diagonalization (SQD) across hardware deployment choices, finding that structured perturbations to CCSD amplitudes cause only modest energy shifts, layout and noise-mitigation differences narrow within a few iterations, and accuracy saturates at moderate shot budgets with very large budgets slightly worsening energies.
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.