Hardware Robustness of Sample-Based Quantum Diagonalization
2026-07-20 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors studied a method called Sample-based Quantum Diagonalization (SQD), which helps find energy levels in quantum systems using both quantum and classical computing. They tested how well SQD works under different practical conditions like noise, qubit arrangements, and starting guesses. They found that even if the starting guesses are heavily altered or zeroed out, SQD still gives mostly accurate energy results after a few steps. While initial performance varies, the method stabilizes quickly, and using very large sample sizes doesn't always improve accuracy. Their work shows which parts of SQD are truly reliable and where improvements are needed.
Sample-based Quantum Diagonalizationquantum computingnoise mitigationqubit layoutcoupled-cluster singles and doubles (CCSD)variational optimizationquantum processing unit (QPU)shot budgetansatz initialization
Authors
Ahatesham Bhuiyan, Cheng Chu, Qian Lou, Mengxin Zheng
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.