Quantum Simulations of Solvated Biomolecules Using Hybrid Methods

Honghui Shang, Fei Wang, Yi Fan +12 more

Jacques P. Bothma, Joel Gilmore, Ross H. McKenzie

Preet Sharma

Marcel Schmidt am Busch, Anne Lopes, David Mignon +2 more

Madhava S. Niraghatam, Shubham D. Yadav, Sumanta K. Ghosh

We present a hybrid quantum-classical framework for molecular simulations that combines the variational quantum eigensolver (VQE) with projection-based embedding (PBE) and an implicit solvation treatment. Solvent effects are incorporated through the solvation model based on density (SMD), enabling the investigation of chemically relevant processes in condensed phase environments within a quantum computing workflow. The embedding strategy partitions the molecular system into active and environment subsystems, allowing correlated quantum simulations of the chemically important subsystem while retaining an efficient classical description of the surroundings. As a representative application, we compute the potential energy surface of a bimolecular nucleophilic substitution (SN2) reaction between the chloride ion and chloromethane in multiple solvents, demonstrating that quantum eigensolver methods can be extended beyond gas-phase models to include solvent effects relevant for reaction energetics.

Fang Liu, David M. Sanchez, Heather J. Kulik +1 more

Abstract The conductor‐like polarizable continuum model (C‐PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in quantum chemistry. We have previously implemented C‐PCM solvation for Hartree‐Fock (HF) and density functional theory (DFT) on graphical processing units (GPUs), enabling the quantum mechanical treatment of large solvated biomolecules. Here, we first propose a GPU‐based algorithm for the PCM conjugate gradient linear solver that greatly improves the performance for very large molecules. The overhead for PCM‐related evaluations now consumes less than 15% of the total runtime for DFT calculations on large molecules. Second, we demonstrate that our algorithms tailored for ground state C‐PCM are transferable to excited state properties. Using a single GPU, our method evaluates the analytic gradient of the linear response PCM time‐dependent density functional theory energy up to 80× faster than a conventional central processing unit (CPU)‐based implementation. In addition, our C‐PCM algorithms are transferable to other methods that require electrostatic potential (ESP) evaluations. For example, we achieve speed‐ups of up to 130× for restricted ESP‐based atomic charge evaluations, when compared to CPU‐based codes. We also summarize and compare the different PCM cavity discretization schemes used in some popular quantum chemistry packages as a reference for both users and developers.