Year: 2026
Type: article
Abstract: An accurate description of strong correlation is quintessential for the exploration of emerging chemical phenomena. While near-term variational quantum algorithms provide a theoretically scalable framework for quantum chemical problems, the accurate simulation of multireference effects remains elusive, hindering progress toward the rational design of novel chemical space. In this regard, we introduce a general and customizable scheme to handle strong electronic correlation, based on problem decomposition, entanglement buildup, and subsequent consolidation. Based on a problem-inspired molecular decomposition, the deployment of hardware efficient ansatz to prepare entangled subsystems ensures efficient construction of a multireference state while concurrently adhering to the hardware topology. The dynamic correlation is subsequently introduced through a unitary coupled cluster framework, with a static or dynamic ansatz parametrized by a set of interfragment generalized operators, and with the product state spanning various subsystems taken as the reference. The hybrid architecture ensures a judicious deployment of separate ansatze structures for capturing various degrees of correlation in a balanced manner, while concurrently retaining the scalability and flexibility provided by them individually. Across several numerical applications to strongly correlated systems, the proposed scheme shows encouraging performance in terms of accuracy, flexibility, and resources, suggesting its potential usefulness for exploring quantum algorithms in quantum chemistry.
Authors: Arpan Choudhury, Sonaldeep Halder, Rahul Maitra, Debashree Ghosh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G5423456983, G76143323
OpenAlex ID: W4415883163
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