Molecular Dynamics

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Molecular Dynamics

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Interatomic Potentials

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Density Functional Theory

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First-Principles Calculation

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Large-Scale Electronic Structure Calculations

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Jun 29, 2026 · quant-phJ/K move · Enter open · S save

Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Ruchika Bhat+3

Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the

\mathcal{O}(N^6)

CCSD parameter initialization of the competing LUCJ ansatz approach with

\mathcal{O}(N^4)

MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N

_2

molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C

_{10}

_{17}

N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C

_{15}

_{28}

_4

_5

S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.

Computational Chemistry Structure-Based Drug Design Molecular Dynamics Drug Discovery Fault Tolerance

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