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Quantum-Centric Alchemical Free Energy Calculations
Milana Bazayeva1, Zhen Li1, Danil Kaliakin1
1Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
This study integrates quantum computing into alchemical free energy (AFE) predictions using a hybrid quantum-classical workflow. Configuration interaction (CI) calculations on quantum hardware improve hydration free energy predictions, showing promise for future drug discovery and biomolecular simulations.
Area of Science:
- Computational Chemistry
- Quantum Computing
- Biomolecular Simulations
Background:
- Alchemical free energy (AFE) predictions are crucial for understanding molecular interactions.
- Current methods often rely on classical approximations, limiting accuracy for systems with significant electronic correlation.
- Integrating high-level electronic structure methods into AFE workflows is challenging.
Purpose of the Study:
- To develop and validate a hybrid quantum-classical workflow for AFE predictions.
- To incorporate configuration interaction (CI) calculations into the book-ending framework.
- To assess the feasibility of using quantum processing units (QPUs) for accurate free energy calculations.
Main Methods:
- Extended the book-ending framework with a hybrid quantum-classical workflow.
- Developed an interface for CI calculations using classical (PySCF) and quantum-centric (SQD, ext-SQD) backends.
- Computed book-ending corrections for hydration free energies (HFEs) of small organic molecules using quantum hardware.
Main Results:
- Successfully incorporated CI-level electronic structure calculations into AFE workflows.
- CI-corrected HFEs showed reasonable agreement with experimental values.
- Demonstrated the feasibility of QPU-accelerated free energy predictions.
Conclusions:
- Hybrid quantum-classical workflows can enhance the accuracy of AFE predictions.
- Quantum hardware offers a scalable route to CI-quality electronic structure data.
- This approach has potential applications in modeling molecular recognition, enzyme catalysis, and drug-receptor interactions.
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