2025/11/18 by Hao Zhang, Zhang, Hao, Matthew Otten +1 · 1 voice · 3 citations
Computer Science · Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum many-body systems #Strongly Correlated Electrons (cond-mat.str-el) #cond-mat.str-el #physics.chem-ph #quant-ph
paper · pdf · doi:10.48550/arxiv.2511.14734
openalex publication_date 2025/11/18 · arxiv published 2025/11/18 · arxiv updated 2025/11/18 · openalex created_date 2025/11/20 · openalex updated_date 2026/07/28
Accurate quantum many-body calculations often depend on reliable reference states or good human-designed ansätze, yet these sources of knowledge can become unreliable in hard problems like strongly correlated systems. We introduce the Trimmed Configuration Interaction (TrimCI) method, a prior-knowledge-free algorithm that builds accurate ground states directly from random Slater determinants. TrimCI iteratively expands the variational space and trims away unimportant states, allowing a random initial core to self-refine into an accurate approximation of exact ground state. Across challenging benchmarks, TrimCI achieves state-of-the-art accuracy with strikingly efficiency gains of several orders of magnitude. For [4Fe-4S] cluster, it matches recent quantum computing results with 106-fold fewer determinants and CPU-hours. For the nitrogenase P-cluster, it matches selected-CI accuracy using 105-fold fewer determinants. For 8×8 Hubbard model, it recovers over 99% of the ground-state energy using only 10-28 of the Hilbert space. In some regimes, TrimCI attains orders-of-magnitude higher accuracy than AFQMC method. These results demonstrate that high-accuracy many-body ground states can be discovered directly from random determinants, establishing TrimCI as a prior-knowledge-free, accurate and highly efficient framework for quantum many-body systems. The compact explicit wavefunctions it produces further enable direct and rapid evaluation of observables.