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International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)

Adaptive Bias Fields Quantum Approximate Optimization Algorithm for Molecular Docking

Gang Liu, Jiapei Zhuang

Published Oct 24, 2025• 1 citation
Publisher

Abstract

Molecular docking, a critical task in drug discovery, represents a highly promising application domain for quantum computing. While the Quantum Approximate Optimization Algorithm (QAOA) shows potential, its performance on near-term quantum devices is limited by noise susceptibility and optimization challenges such as barren plateaus. To address these limitations, we apply Adaptive Bias Fields QAOA (ABF-QAOA) to solve molecular docking problems mapped to Ising Hamiltonians. Our results demonstrate that ABF-QAOA achieves accelerated convergence and higher solution accuracy compared to standard QAOA. In addition, under simulated quantum noise conditions, ABF-QAOA maintains higher solution quality, showcasing significantly enhanced noise resilience. This work establishes ABF-QAOA as a robust quantum approach for molecular docking on noisy intermediate-scale quantum (NISQ) hardware.

DOI
10.1109/ccpqt66408.2025.11382648

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Molecular docking, a critical task in drug discovery, represents a highly promising application domain for quantum computing. While the Quantum Approximate Optimization Algorithm (QAOA) shows potential, its performance on near-term quantum devices is limited by noise susceptibility and optimization challenges such as barren plateaus. To address these limitations, we apply Adaptive Bias Fields QAOA (ABF-QAOA) to solve molecular docking problems mapped to Ising Hamiltonians. Our results demonstrate that ABF-QAOA achieves accelerated convergence and higher solution accuracy compared to standard QAOA. In addition, under simulated quantum noise conditions, ABF-QAOA maintains higher solution quality, showcasing significantly enhanced noise resilience. This work establishes ABF-QAOA as a robust quantum approach for molecular docking on noisy intermediate-scale quantum (NISQ) hardware.
Molecular docking is a critical process for drug discovery and challenging due to the complexity and size of biomolecular systems, where the optimal binding configuration of a drug to a target protein is determined. Hybrid classical-quantum computing techniques offer a novel approach to address these challenges. The Quantum Approximate Optimization Algorithm (QAOA) and its variations are hybrid classical-quantum techniques, and a promising tool for combinatorial optimization challenges. This paper presents a Digitized Counterdiabatic QAOA (DC-QAOA) approach to molecular docking. Simulated quantum runs were conducted on a GPU cluster. We examined 14 and 17 nodes instances - to the best of our knowledge the biggest published instance is 12-node at Ding et al. and we present the results. Based on computational results, we conclude that binding interactions represent the anticipated exact solution. Additionally, as the size of the examined instance increases, the computational times exhibit a significant escalation.
Molecular docking plays a pivotal role in drug discovery and precision medicine, furnishing insights into protein functionalities and fostering the development of therapeutics. Here, we introduce a potential alternative solution to this problem using a quantum approximate optimization algorithm (QAOA) based algorithm. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro{\mathrm{M}}^{\text{pro}}Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. We found that the digitized-counterdiabatic QAOA (DCQAOA), which integrates the concept of counterdiabatic driving, surpasses the conventional QAOA in terms of quantum circuit depth and optimization efficiency. This is particularly evident in complex molecular-docking challenges, where DCQAOA delivers more accurate and biologically relevant results. Our research highlights the promising role of quantum computing in drug discovery and contributes significant insights towards the optimization of protein-ligand docking methodologies.
The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems on noisy intermediate-scale quantum (NISQ) hardware. Making NISQ implementations of the QAOA resilient to noise requires short ansatz circuits with as few controlled- () gates as possible. Here we present the dynamic adaptive quantum approximate optimization algorithm (Dynamic-ADAPT-QAOA). Our algorithm significantly reduces the circuit depth and the count of standard ADAPT-QAOA, a leading proposal for near-term implementations of the QAOA. Throughout our algorithm, the decision to apply -intensive operations is made dynamically, based on algorithmic benefits. Using density-matrix simulations, we benchmark the noise resilience of ADAPT-QAOA and Dynamic-ADAPT-QAOA. We compute the gate-error probability p gate ★ below which these algorithms provide, on average, more accurate solutions than the classical, polynomial-time approximation algorithm by Goemans and Williamson. For small systems with six to ten qubits, we show that p gate ★ > 10 − 3 for Dynamic-ADAPT-QAOA. Compared to standard ADAPT-QAOA, this constitutes an order-of-magnitude improvement in noise resilience. This improvement should make Dynamic-ADAPT-QAOA viable for implementations on superconducting NISQ hardware, even in the absence of error mitigation. Published by the American Physical Society 2024
The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term quantum devices. When employing the QAOA to 3-SAT and Max-3-SAT problems, the quantum cost exhibits an easy-hard-easy or easy-hard pattern, respectively, as the clause density is changed. The quantum resources needed in the hard-region problems are out of reach for current noisy intermediate-scale quantum (NISQ) devices. We show by numerical simulations with up to 14 variables and analytical arguments that the adaptive-bias QAOA (ab-QAOA) greatly improves performance in the hard region of the 3-SAT problems and hard region of the Max-3-SAT problems. For similar accuracy, on average, ab-QAOA needs 3 levels for 10-variable 3-SAT problems as compared to 22 for QAOA. For 10-variable Max-3-SAT problems, the numbers are 7 levels and 62 levels. The improvement comes from a more targeted and more limited generation of entanglement during the evolution. We demonstrate that classical optimization is not strictly necessary in the ab-QAOA since local fields are used to guide the evolution. This leads us to propose an optimization-free ab-QAOA that can solve the hard-region 3-SAT and Max-3-SAT problems effectively with significantly fewer quantum gates as compared to the original ab-QAOA. Our work paves the way for realizing quantum advantages for optimization problems on NISQ devices.
This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates. Our approach decomposes large QAOA problems into smaller subproblems, distributing them across multiple Quantum Processing Units (QPUs) to enhance scalability and performance. The noise-aware strategy incorporates error mitigation techniques to optimize qubit fidelity and gate operations, ensuring reliable quantum computations. We evaluate the efficacy of our framework using the HamilToniQ Benchmarking Toolkit, which quantifies the performance across various quantum hardware configurations. The results demonstrate that our distributed QAOA framework achieves significant improvements in computational speed and accuracy, showcasing its potential to solve complex optimization problems efficiently in the NISQ era. This work sets the stage for advanced algorithmic strategies and practical quantum system enhancements, contributing to the broader goal of achieving quantum advantage.
The Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for solving combinatorial optimization problems on near-term quantum hardware. However, its practical deployment remains severely limited by hardware-induced noise and decoherence. In the absence of full fault tolerance, Quantum Error Mitigation (QEM) techniques offer a viable approach to enhance QAOA performance on noisy intermediate-scale quantum (NISQ) devices. In this work, we present a unified classification of QEM strategies specifically tailored for QAOA, systematically categorized into preprocessing, during execution, and post-processing phases. We further classify QAOA deployment settings—such as hardware-constrained devices, stochastic noise-dominated environments, and dense graphs problem—and recommend appropriate QEM techniques for each. A comparative analysis highlights the trade-offs among mitigation accuracy, resource overhead, and robustness to different noise types. By synthesizing recent advances and identifying open challenges, our paper provides both a comprehensive survey and a practical framework for designing robust, noise-resilient QAOA pipelines on near-term quantum platforms.