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

Abstract

Strongly correlated electronic systems exhibit inherently multiconfigurational wave functions, making it difficult to construct compact variational subspaces that preserve the essential multireference character. Quantum computing has emerged as a promising route to alleviate these limitations, and sample-based quantum diagonalization (SQD) is a representative hybrid approach that uses quantum hardware as a determinant sampler followed by classical diagonalization in the projected subspace. To mitigate hardware noise, SQD employs a self-consistent particle-number recovery guided by a single global reference occupancy vector. However, in strongly correlated, multimodal regimes, this global reference can become mixture-averaged and bias recovery toward a mean pattern, diluting mode-specific occupation structure and degrading the determinant pool. Here, we introduce cluster-adaptive SQD (CSQD), which clusters measurement samples via unsupervised learning and performs particle-number recovery using cluster-specific, self-consistently updated reference occupancy vectors. Under a matched variational budget, we benchmarked CSQD against SQD for N2 dissociation in a (10e,26o) active space and the [2Fe-2S] cluster in a (30e,20o) active space. Our results indicate that CSQD offers an advantage over SQD in estimating the ground-state energy in the strongly correlated regime, lowering the variational estimate by up to 15.95 mHa for stretched N2 and up to 45.53 mHa for [2Fe-2S], with modest additional classical overhead.