From Random Determinants to the Ground State
Hao Zhang, Matthew Otten

TL;DR
TrimCI is a novel, prior-knowledge-free algorithm that constructs accurate quantum many-body ground states from random determinants, achieving state-of-the-art accuracy with significant efficiency improvements across challenging benchmarks.
Contribution
It introduces TrimCI, a self-refining method that builds accurate ground states directly from random determinants, eliminating the need for human-designed ansätze or reliable reference states.
Findings
Achieves state-of-the-art accuracy with orders-of-magnitude efficiency gains.
Matches recent quantum computing results with vastly fewer determinants.
Recovers over 99% of ground-state energy in large Hubbard models.
Abstract
Accurate quantum many-body calculations often depend on reliable reference states or good human-designed ans\"atze, 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 -fold fewer determinants and CPU-hours. For the nitrogenase P-cluster, it matches selected-CI accuracy…
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Taxonomy
TopicsMachine Learning in Materials Science · Quantum many-body systems · Quantum Computing Algorithms and Architecture
