Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer

• URL: http://arxiv.org/abs/2608.19868v1
• Date: Thu, 20 Aug 2026 10:27:47 GMT
• Title: Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer
• Abstract summary: We propose a hybrid quantum-classical approach for molecular docking.<n>We prove that a global minimizer of the FBE objective can always be chosen to be a pure product state.<n>We also execute the circuit on an IBM quantum computer.
• Score: 16.44284103525396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular docking is a vital computational task in drug discovery, wherein the objective is to efficiently identify optimal binding poses between a ligand and a target receptor protein. Due to the combinatorial explosion of possible binding configurations, docking of large and flexible molecules remains a computationally intensive problem, especially at scale. Early studies have revealed that the molecular docking can be re-cast as a maximum vertex-weighted clique problem (MVWCP) problem on a compatibility graph to be solved classically. In this work, we proposed a hybrid quantum-classical approach for molecular docking leveraging the MVWCP formalism with a variational full-basis encoding (FBE) strategy, which enables efficient encoding of classical binary variables with Bloch sphere vectors. We further prove that a global minimizer of the FBE objective can always be chosen to be a pure product state, thereby providing a rigorous justification for its optimization using a unitary variational circuit. The molecular docking problem is first mapped to a cost Hamiltonian that is minimized within a variational framework, optimized via a randomized imaginary time evolution (ITE)-inspired warm start, and gradient-based techniques. Finally, we also executed the circuit on an IBM quantum computer, underlying the feasibility and of quantum-assisted optimization for structure-based drug design and point towards the broader utility of advanced encoding techniques in quantum optimization for computational biology.

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