Quantum Computing Enabled ab initio Molecular Dynamics Simulations

2026/07/30 by Susanta Das, Subhamoy Bhowmik, Zhen Li +5
Physics and Astronomy · #physics.chem-ph #quant-ph

paper · pdf

45 pages, 7 figures. Supporting Information included as an ancillary file

Abstract

We demonstrate a quantum-classical workflow for ab initio molecular dynamics (AIMD) in which quantum measurements from a chemistry-inspired LUCJ ansatz are post-processed using Sample-based Quantum Diagonalization (SQD) to recover determinant subspaces and deliver energies and analytical nuclear gradients for dynamics. As an exact benchmark, we use full configuration interaction (FCI) in the STO-3G basis, enabling a direct assessment of the accuracy of SQD. In gas-phase benchmarks, SQD reproduces FCI energies and gradients to within 1 kcal mol^-1 of the FCI reference and yields stable AIMD trajectories. In explicit-solvent QM/MM simulations, SQD retains this agreement, matching FCI energy fluctuations and RMS gradient profiles and reproducing solute-solvent structure as quantified by radial distribution functions. Overall, these benchmarks establish LUCJ+SQD as a practical route for integrating current quantum hardware into QM/MM molecular dynamics and provide an early demonstration of condensed-phase QM/MM dynamics driven by a quantum electronic-structure engine.

Related

• Implicit solvent sample-based quantum diagonalization
• Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
• Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework
• Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework
• Convergence of sample-based quantum diagonalization on a variable-length cuprate chain