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arXiv · 2603.09346

Cluster-Adaptive Sample-Based Quantum Diagonalization for Strongly Correlated Systems

Byeongyong Park·Sanha Kang·Jongseok Seo·Juhee Baek·Doyeol Ahn·Keunhong Jeong
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Abstract

Sample-based quantum diagonalization (SQD) is a hybrid quantum-classical algorithm for estimating ground-state energies in electronic-structure calculations. It uses a quantum processor as a sampler to construct a variational subspace, with Hamiltonian projection and diagonalization performed classically. A critical step in SQD is self-consistent particle-number recovery guided by a global reference occupancy vector. In strongly correlated systems, however, dominant determinants can be distributed across regions of determinant space, causing this reference to become mixture-averaged and biasing recovery toward mean occupations. Here, we introduce cluster-adaptive SQD (CSQD), which clusters pooled single-spin strings and performs particle-number recovery using cluster-specific reference occupancy vectors. Under a matched variational budget, CSQD lowers ground-state energies relative to SQD by up to 15.95 mHa for stretched N2 in a (10e,26o) active space and 57.82 mHa for [2Fe-2S] in a (30e,20o) active space. These results suggest that CSQD better captures dispersed occupation structure in strongly correlated systems.

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Byeongyong Park, Sanha Kang, Jongseok Seo, Juhee Baek, Doyeol Ahn, Keunhong Jeong. 2026-03-10. Cluster-Adaptive Sample-Based Quantum Diagonalization for Strongly Correlated Systems. https://arxiv.org/abs/2603.09346

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