Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Methods

Exploring quantum frontiers in protein structure prediction: techniques, challenges, and opportunities

Arunkumar Selvaraj, Maharsh Jayawant, Nagarajan Kayalvizhi, Muthukalingan Krishnan, Nallapermumal Puvaneshvari, Aruni Wilson Santhosh Kumar, Neelamegam Rameshkumar

Published Apr 16, 2026• 1 citation
Publisher
DOI
10.1016/j.ymeth.2026.04.006

Related papers

Exploring the Frontiers of Quantum Machine Learning: State of the Art Challenges and Applications

Mikel Prieto

•Jul 6, 2026•0 citations

Quantum Artificial Intelligence for Protein Structure Prediction

Don Roosan, Rubayat Islam Khan

Springer eBooks•Jan 1, 2026•0 citations

Quantum Bioinformatics and Protein Structure Prediction

Jincheng Zhang

This paper explores the potential of integrating quantum computing with bioinformatics to revolutionize protein structure prediction. Traditional methods face significant computational challenges, particularly with complex proteins. We propose a novel approach leveraging quantum algorithms to accelerate molecular simulations and integrate biological data for enhanced prediction accuracy. The core claim centers on the ability to achieve more precise protein structure determination through this combined methodology. Specifically, we outline a mechanism utilizing quantum algorithms to drastically reduce simulation times and subsequently, incorporate biological information for optimization. This represents a new paradigm in protein structure prediction, offering a pathway to accurately model protein folding and dynamics. The work delves into the theoretical foundations and potential algorithmic implementations, laying the groundwork for future research and development in this exciting field. The focus is on the computational speedup offered by quantum approaches and the strategic integration with existing bioinformatics techniques.

•Sep 6, 2026•0 citations

Development of quantum machine learning for protein structure prediction

Nimbe Qureshi Bianco, Sierra-Sosa Miyashita, Pathak Pathak

Quantum Machine Learning (QML) holds immense potential in revolutionizing the prediction of protein structures, a critical challenge in computational biology. This research explores the application of quantum states, including superposition and entanglement, to capture the intricate and uncertain nature of protein conformations. Quantum gates and Fourier transforms are investigated as tools to manipulate and enhance quantum states, showcasing their ability to discern features essential for accurate predictions. The integration of hybrid quantum-classical models addresses the current limitations of quantum hardware, combining classical and quantum computing strengths. Quantum error correction is identified as a pivotal aspect for ensuring the reliability of predictions in the quantum domain. A numerical example is presented to illustrate the probabilistic nature of quantum states and the potential for obtaining optimized outcomes through quantum machine learning. The findings highlight the need for continued interdisciplinary collaboration between quantum physicists, computer scientists, and computational biologists to advance the field. While the exploration of QML for Protein Structure Prediction is in its early stages, the research emphasizes the transformative potential of quantum computing in unraveling the complexities of molecular structures.

Journal of Computer Science and Research (JoCoSiR)•Nov 30, 2023•1 citation

Fast quantum algorithm for protein structure prediction in hydrophobic-hydrophilic model

Renata Wong, Weng-Long Chang

Journal of Parallel and Distributed Computing•Mar 21, 2022•35 citations

A Quantum-Classical Hybrid System for Protein Folding

Manikanta Gandivalasa, Vamsi Bandana, Chandini Patnaikuni +2 more

Protein folding is a computationally intensive problem in bioinformatics that involves predicting the three-dimensional structure of a protein from its amino acid sequence. Classical approaches face challenges due to the exponential complexity of the search space. In this paper, we propose Q-ProteinFold, a quantum-classical hybrid system that leverages the Variational Quantum Eigensolver (VQE) for protein folding approximation. The system encodes protein sequences into parameterized quantum circuits using Qiskit and explores multiple conformations through quantum superposition and entanglement. A classical optimizer iteratively minimizes the energy function to identify stable protein structures. The implementation is developed using Python, Streamlit, and Qiskit Aer simulator. Experimental results demonstrate energy convergence, probabilistic state distribution, and effective fold prediction. This work highlights the potential of quantum computing in solving complex biological optimization problems.

•Mar 25, 2026•0 citations
This paper explores the potential of integrating quantum computing with bioinformatics to revolutionize protein structure prediction. Traditional methods face significant computational challenges, particularly with complex proteins. We propose a novel approach leveraging quantum algorithms to accelerate molecular simulations and integrate biological data for enhanced prediction accuracy. The core claim centers on the ability to achieve more precise protein structure determination through this combined methodology. Specifically, we outline a mechanism utilizing quantum algorithms to drastically reduce simulation times and subsequently, incorporate biological information for optimization. This represents a new paradigm in protein structure prediction, offering a pathway to accurately model protein folding and dynamics. The work delves into the theoretical foundations and potential algorithmic implementations, laying the groundwork for future research and development in this exciting field. The focus is on the computational speedup offered by quantum approaches and the strategic integration with existing bioinformatics techniques.
Quantum Machine Learning (QML) holds immense potential in revolutionizing the prediction of protein structures, a critical challenge in computational biology. This research explores the application of quantum states, including superposition and entanglement, to capture the intricate and uncertain nature of protein conformations. Quantum gates and Fourier transforms are investigated as tools to manipulate and enhance quantum states, showcasing their ability to discern features essential for accurate predictions. The integration of hybrid quantum-classical models addresses the current limitations of quantum hardware, combining classical and quantum computing strengths. Quantum error correction is identified as a pivotal aspect for ensuring the reliability of predictions in the quantum domain. A numerical example is presented to illustrate the probabilistic nature of quantum states and the potential for obtaining optimized outcomes through quantum machine learning. The findings highlight the need for continued interdisciplinary collaboration between quantum physicists, computer scientists, and computational biologists to advance the field. While the exploration of QML for Protein Structure Prediction is in its early stages, the research emphasizes the transformative potential of quantum computing in unraveling the complexities of molecular structures.
Protein folding is a computationally intensive problem in bioinformatics that involves predicting the three-dimensional structure of a protein from its amino acid sequence. Classical approaches face challenges due to the exponential complexity of the search space. In this paper, we propose Q-ProteinFold, a quantum-classical hybrid system that leverages the Variational Quantum Eigensolver (VQE) for protein folding approximation. The system encodes protein sequences into parameterized quantum circuits using Qiskit and explores multiple conformations through quantum superposition and entanglement. A classical optimizer iteratively minimizes the energy function to identify stable protein structures. The implementation is developed using Python, Streamlit, and Qiskit Aer simulator. Experimental results demonstrate energy convergence, probabilistic state distribution, and effective fold prediction. This work highlights the potential of quantum computing in solving complex biological optimization problems.