Towards large-scale ab initio Molecular Dynamics Simulation with Efficient Sampling-based Quantum Computing Algorithms
Abstract
Ab initio molecular dynamics (AIMD) requires the calculation of highly accurate electronic-structure energies and nuclear forces at every time step, which can quickly become computationally expensive. Sample-based quantum diagonalization offers a route to such calculations on quantum hardware, but its use in AIMD simulations has so far been limited to active spaces of about 20 qubits. In these spaces, exact diagonalization is tractable, and the quantum circuits are typically built from standard coupled-cluster amplitudes. Here, we introduce SqDRIFT-AIMD, which combines randomized sample-based Krylov quantum diagonalization (SqDRIFT) with analytical nuclear gradients. With SqDRIFT-AIMD, we obtain more accurate ground-state energies than the conventional coupled-cluster singles and doubles (CCSD) driven AIMD simulation for the $40$-qubit active space. We apply SqDRIFT-AIMD to study the dynamics of proton transfer in the Zundel cation, using circuits executed on IBM Heron r3 quantum processors. SqDRIFT energies fall below CCSD across the $40$-qubit double-well PES and stay within a few mEh of CCSD at 52 qubits. The $40$-qubit SqDRIFT-AIMD trajectory tracks the SHCI-driven proton transfer to within $1.33$ mÅ and exhibits excellent total energy conservation, with a standard deviation of only $0.245$ mEh. These results extend quantum-centric AIMD to active spaces beyond the reach of exact diagonalization.
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BibTeX
@misc{khandelwal2026towards,
title = {{Towards large-scale ab initio Molecular Dynamics Simulation with Efficient Sampling-based Quantum Computing Algorithms}},
author = {Ankit Khandelwal and Nirmal M R and Aaryav Mishra and Debasmita Bhoumik and Dhinakaran Vinayagamurthy and M Girish Chandra},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.11084},
eprint = {2611.11084},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.11084},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Towards large-scale ab initio Molecular Dynamics Simulation with Efficient Sampling-based Quantum Computing Algorithms
AU - Ankit Khandelwal
AU - Nirmal M R
AU - Aaryav Mishra
AU - Debasmita Bhoumik
AU - Dhinakaran Vinayagamurthy
AU - M Girish Chandra
PY - 2026
DA - 2026/11/08
PB - arXiv
DO - 10.48550/arxiv.2611.11084
UR - https://arxiv.org/abs/2611.11084
AB - Ab initio molecular dynamics (AIMD) requires the calculation of highly accurate electronic-structure energies and nuclear forces at every time step, which can quickly become computationally expensive. Sample-based quantum diagonalization offers a route to such calculations on quantum hardware, but its use in AIMD simulations has so far been limited to active spaces of about 20 qubits. In these spaces, exact diagonalization is tractable, and the quantum circuits are typically built from standard coupled-cluster amplitudes. Here, we introduce SqDRIFT-AIMD, which combines randomized sample-based Krylov quantum diagonalization (SqDRIFT) with analytical nuclear gradients. With SqDRIFT-AIMD, we obtain more accurate ground-state energies than the conventional coupled-cluster singles and doubles (CCSD) driven AIMD simulation for the $40$-qubit active space. We apply SqDRIFT-AIMD to study the dynamics of proton transfer in the Zundel cation, using circuits executed on IBM Heron r3 quantum processors. SqDRIFT energies fall below CCSD across the $40$-qubit double-well PES and stay within a few mE_h of CCSD at 52 qubits. The $40$-qubit SqDRIFT-AIMD trajectory tracks the SHCI-driven proton transfer to within $1.33$ mÅ and exhibits excellent total energy conservation, with a standard deviation of only $0.245$ mE_h. These results extend quantum-centric AIMD to active spaces beyond the reach of exact diagonalization.
ER -