Quantum Algorithm for Protein Side-Chain Optimisation: Comparing Quantum to Classical Methods

• URL: http://arxiv.org/abs/2507.19383v1
• Date: Fri, 25 Jul 2025 15:37:04 GMT
• Title: Quantum Algorithm for Protein Side-Chain Optimisation: Comparing Quantum to Classical Methods
• Authors: Anastasia Agathangelou, Dilhan Manawadu, Ivano Tavernelli,
• Abstract summary: We develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures.<n>We propose a quantum algorithm based on the Quantum Approximate optimisation algorithm to explore the conformational space and identify low-energy configurations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modelling and predicting protein configurations is crucial for advancing drug discovery, enabling the design of treatments for life-threatening diseases. A critical aspect of this challenge is rotamer optimisation - the determination of optimal side-chain conformations given a fixed protein backbone. This problem, involving the internal degrees of freedom of amino acid side-chains, significantly influences the protein's overall structure and function. In this work, we develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures, with a focus on side-chain configuration. We formulate the rotamer optimisation problem as a Quadratic Unconstrained Binary Optimisation problem and map it to an Ising model, enabling efficient quantum encoding. Building on this formulation, we propose a quantum algorithm based on the Quantum Approximate Optimisation Algorithm to explore the conformational space and identify low-energy configurations. To benchmark our approach, we conduct a classical study using custom-built libraries tailored for structural characterisation and energy optimisation. Our quantum method demonstrates a reduction in computational cost compared to classical simulated annealing techniques, offering a scalable and promising framework for protein structure optimisation in the quantum era.

Related papers

• An Efficient Quantum Approximate Optimization Algorithm with Fixed Linear Ramp Schedule for Truss Structure Optimization [0.0]
  This study proposes a novel structural optimization framework based on quantum variational circuits.<n>By defining design variables as multipliers, it provides greater flexibility in adjusting the cross-sectional area of the rod.<n>As a result, the objective function is in a simple format, enabling efficient optimization using QAOA.
  arXiv  Detail & Related papers  (2025-02-24T01:19:41Z)
• Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation [3.9857517408503567]
  Quantum Approximate optimisation algorithm (qaoa) is a widely studied quantum-classical iterative for optimisation. We introduce a new algorithmic variant based on non-iterative computation that is instance-independent, but problem-specific. Our approach is based on proving a long-standing conjecture regarding quantum-independent structures in qaoa.
  arXiv  Detail & Related papers  (2024-08-12T21:02:58Z)
• Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization [0.0]
  The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Recent machine learning breakthroughs have enabled accurate and rapid structure predictions. We introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated.
  arXiv  Detail & Related papers  (2024-07-09T18:42:45Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Automatic and effective discovery of quantum kernels [41.61572387137452]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
  We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
  arXiv  Detail & Related papers  (2022-09-05T07:23:03Z)
• Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection [1.9182522142368683]
  We introduce a variational quantum algorithm to solve unconstrained black box binary problems. This is in contrast to the typical setting of quantum algorithms for optimization. We show that the quantum method produces competitive and in certain aspects even better performance than traditional feature selection techniques.
  arXiv  Detail & Related papers  (2022-05-06T07:02:15Z)
• Polynomial unconstrained binary optimisation inspired by optical simulation [52.11703556419582]
  We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization. We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
  arXiv  Detail & Related papers  (2021-06-24T16:39:31Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• Quantum variational optimization: The role of entanglement and problem hardness [0.0]
  We study the role of entanglement, the structure of the variational quantum circuit, and the structure of the optimization problem. Our numerical results indicate an advantage in adapting the distribution of entangling gates to the problem's topology. We find evidence that applying conditional value at risk type cost functions improves the optimization, increasing the probability of overlap with the optimal solutions.
  arXiv  Detail & Related papers  (2021-03-26T14:06:54Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.