Cluster-Adaptive Sample-Based Quantum Diagonalization for Strongly Correlated Systems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910145387167744 |
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| author | Park, Byeongyong Kang, Sanha Seo, Jongseok Baek, Juhee Ahn, Doyeol Jeong, Keunhong |
| author_facet | Park, Byeongyong Kang, Sanha Seo, Jongseok Baek, Juhee Ahn, Doyeol Jeong, Keunhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09346 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cluster-Adaptive Sample-Based Quantum Diagonalization for Strongly Correlated Systems Park, Byeongyong Kang, Sanha Seo, Jongseok Baek, Juhee Ahn, Doyeol Jeong, Keunhong Quantum Physics 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. |
| title | Cluster-Adaptive Sample-Based Quantum Diagonalization for Strongly Correlated Systems |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.09346 |