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◆ Journal of Chemical Theory and Computation2026-06-15· Workflow

Quantum-Centric Alchemical Free Energy Calculations

Milana Bazayeva, Zhen Li, Danil Kaliakin, Fangchun Liang, Akhil Shajan, Susanta Kumar Das, Kenneth M. Merz

原始摘要(英文原文)· Original abstract
In this work, we extended the book-ending framework with a hybrid quantum-classical workflow that incorporates configuration interaction (CI) calculations into alchemical free energy (AFE) predictions. In the book-ending approach, the Multistate Bennett Acceptance Ratio (MBAR) is applied along a coupling parameter λ to interpolate the system from molecular mechanics (MM) (λ = 0) to a quantum mechanics (QM) (λ = 1) description, and the resulting correction is added to the classically computed AFE. Building on the standard book-ending workflow, we developed an interface that introduces the CI contribution through two backends: (I) a classical PySCF-based backend; (II) a quantum-centric sample-based quantum diagonalization (SQD) method and its extended version (ext-SQD). This latter approach combines real quantum processing units (QPUs) with classical postprocessing to obtain CI energies and gradients. To validate the proposed infrastructure, we computed the book-ending corrections for the hydration free energies (HFEs) of three small organic molecules: ammonia, methane, and water. These benchmarks demonstrate that the CI-level electronic structure calculations, particularly those performed on a quantum hardware, can be naturally incorporated into AFE workflows. Specifically, the CI-corrected HFEs are in reasonable agreement with experimental values, supporting the feasibility of QPU-accelerated free energy predictions. As quantum devices continue to improve in scale and fidelity, they might offer a practical and scalable route to CI-quality electronic-structure data for systems that are challenging for classical approaches. Integrating these CI energies directly into QM/MM simulations could improve the accuracy of free energy methods for systems where electronic correlation plays a significant role, with potential relevance to large biomolecular systems, enhancing our ability to model molecular recognition, enzyme catalysis, and drug-receptor interactions.
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