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

AIP Advances

Quantum walk and fast protein-folding time

Fei Wan, Li-Hua Lü, You-Quan Li

Published Dec 1, 2025• 0 citations
Publisher

Abstract

The Levinthal paradox states that it would be impossible for a protein to reach its native conformation in a physically meaningful time by randomly searching over all possible conformations. So far, it has been widely assumed that this random conformational search does not occur in the folding process, for which various putative hypotheses have had to be employed in model studies. Here we show that the quantum approach, describing the folding process as a quantum walk on a definite graph, gives rise to a fast protein-folding time. In terms of the conventional lattice model describing protein conformation, we introduce a procedure to enumerate all possible structures and then determine the definite connection graphs. Evaluating the mean first-passage time, we find it is much shorter than that obtained via random searching, and the magnitude of the quantum approach is reasonably in agreement with the experimental result. Some further discussions of various characteristic average quantities are also made by comparing both cases of quantum walk and classical random walk.

DOI
10.1063/5.0293423

Related papers

Quantum Approach to Fast Protein-Folding Time*

Li-Hua Lu, You-Quan Li

In the traditional random-conformational-search model, various hypotheses with a series of meta-stable intermediate states were proposed to resolve the Levinthal paradox in protein-folding time. Here we introduce a quantum strategy to formulate protein folding as a quantum walk on a definite graph, which provides us a general framework without making hypotheses. Evaluating it by the mean of first passage time, we find that the folding time via our quantum approach is much shorter than the one obtained via classical random walks. This idea is expected to evoke more insights for future studies.

arXiv:1906.09184•Chinese Physics Letters•Aug 1, 2019•12 citations

Quantum intelligence on protein folding pathways*

Wen-Wen Mao, Li-Hua Lv, Yong-Yun Ji +1 more

We study the protein folding problem on the base of our quantum approach by considering the model of protein chain with nine amino-acid residues. We introduce the concept of distance space and its projections on a XY-plane, and two characteristic quantities, one is called compactness of protein structure and another is called probability ratio involving shortest path. The concept of shortest path enables us to reduce the 388 × 388 density matrix to a 2 × 2 one from which the von Neumann entropy reflecting certain quantum coherence feature is naturally defined. We observe the time evolution of average distance and compactness solved from the classical random walk and quantum walk, we also compare the features of the time-dependence of Shannon entropy and von Neumann entropy. All the results not only reveal the fast quantum folding time but also unveil the existence of quantum intelligence hidden behind in choosing protein folding pathways.

arXiv:1907.07179•Chinese Physics B•Dec 9, 2019•4 citations

Protein folding using quantum computers

Rajat Kumar Vishwakarma

Protein folding has been one of the most difficult problems for over a half-century,The random thermal motions causing conformational changes that lead energetically downhill towards the native structure, a principle captured in funnel-shaped energy landscapes.Unfolded polypeptides have a wide range of possible conformations. The search problem becomes intractable for classical computers due to the exponential growth of potential conformations with chain length. So far, there is theoretical and experimental evidence that solving such optimization problems using Quantum Computing approaches such as Quantum Annealing, VQE, and QAOA has an advantage. Although Google's DeepMind-AlphaFold has accomplished much,But we can go even further with the quantum approach.Here we show how to predict structure of protein as well as RNA folding using the Variational Quantum Eigensolver with Conditional Value at Risk (CVaR) expectation values for the solution of the problem and for finding the minimum configuration energy and our task is to identify a protein's minimal energy structure. The protein's structure is optimized to reduce energy. Also making sure that all physical constraints are met and encoding the protein folding problem into a qubit operator.

•Oct 4, 2022•0 citations

Quantum Algorithmic Approaches to Protein Folding: A Comprehensive Study of Diverse Lattice Structures

Venugopal K, Anagha Muralidharan, Cherishma Kumar Subhasa +2 more

In computational biology, protein folding continues to be a key challenge with important implications for drug design and biological function comprehension. Using both classical and quantum computing methods, this paper provides an in-depth analysis of various possible lattice configurations and how theyare used in protein folding. To assess the protein design, we investigate classic models including the more intricate Miyazawa-Jernigan (MJ) interaction matrix and the hydrophobic-hydrophilic (HP) lattice model. Furthermore, we use integer programming methods to protein folding problems on lattices, addressing their NP-hardness and proving their effectiveness in obtaining optimal solutions. Expanding upon these conventional techniques, we present sophisticated graph neural network (GNN) models, particularly the RG-MPNN, which integrate pharmacophore ideas to predict chemical properties by using task-related representations. In the latter section of our paper, we examine how protein folding simulations on lattice structures could be transformed using quantum algorithms. Compared to classical methods, quantum computational techniques promise to be more accurate and efficient at handling the enormous diversity and complexity of protein folding.

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)•Nov 7, 2024•1 citation

Efficient quantum algorithm for lattice protein folding

Youle Wang, Xiangzhen Zhou

Abstract Predicting a protein’s three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a fitting domain for applying quantum annealing, an algorithmic technique posited to be faster than its classical counterparts. Nevertheless, the utility of quantum annealing is intrinsically contingent upon the spectral gap associated with the Hamiltonian of lattice proteins. This critical dependence introduces a limitation to the efficacy of these techniques, particularly in the context of simulating the intricate folding processes of proteins. In this paper, we address lattice protein folding as a polynomial unconstrained binary optimization problem, devising a hybrid quantum–classical algorithm to determine the minimum energy conformation effectively. Our method is distinguished by its logarithmic scaling with the spectral gap, conferring a significant edge over the conventional quantum annealing algorithms. The present findings indicate that the folding of lattice proteins can be achieved with a resource consumption that is polynomial in the lattice protein length, provided an ansatz state that encodes the target conformation is utilized. We also provide a simple and scalable method for preparing such states and further explore the adaptation of our method for extension to off-lattice protein models. This work paves a new avenue for surmounting complex computational biology problems via the utilization of quantum computers.

Quantum Science and Technology•Dec 17, 2024•8 citations

Protein folding using quantum computers

Rajat Kumar Vishwakarma

Protein folding has been one of the most difficult problems for over a half-century,Therandom thermal motions causing conformational changes that lead energeticallydownhill towards the native structure, a principle captured in funnel-shaped energylandscapes.Unfolded polypeptides have a wide range of possible conformations. Thesearch problem becomes intractable for classical computers due to the exponentialgrowth of potential conformations with chain length. So far, there is theoretical andexperimental evidence that solving such optimization problems using QuantumComputing approaches such as Quantum Annealing, VQE, and QAOA has anadvantage. Although Google's DeepMind-AlphaFold has accomplished much,But wecan go even further with the quantum approach.Here we show how to predict structureof protein as well as RNA folding using the Variational Quantum Eigensolver withConditional Value at Risk (CVaR) expectation values for the solution of the problem andfor finding the minimum configuration energy and our task is to identify a protein'sminimal energy structure. The protein's structure is optimized to reduce energy. Alsomaking sure that all physical constraints are met and encoding the protein foldingproblem into a qubit operator.

•Oct 2, 2022•0 citations
The Levinthal paradox states that it would be impossible for a protein to reach its native conformation in a physically meaningful time by randomly searching over all possible conformations. So far, it has been widely assumed that this random conformational search does not occur in the folding process, for which various putative hypotheses have had to be employed in model studies. Here we show that the quantum approach, describing the folding process as a quantum walk on a definite graph, gives rise to a fast protein-folding time. In terms of the conventional lattice model describing protein conformation, we introduce a procedure to enumerate all possible structures and then determine the definite connection graphs. Evaluating the mean first-passage time, we find it is much shorter than that obtained via random searching, and the magnitude of the quantum approach is reasonably in agreement with the experimental result. Some further discussions of various characteristic average quantities are also made by comparing both cases of quantum walk and classical random walk.
In the traditional random-conformational-search model, various hypotheses with a series of meta-stable intermediate states were proposed to resolve the Levinthal paradox in protein-folding time. Here we introduce a quantum strategy to formulate protein folding as a quantum walk on a definite graph, which provides us a general framework without making hypotheses. Evaluating it by the mean of first passage time, we find that the folding time via our quantum approach is much shorter than the one obtained via classical random walks. This idea is expected to evoke more insights for future studies.
We study the protein folding problem on the base of our quantum approach by considering the model of protein chain with nine amino-acid residues. We introduce the concept of distance space and its projections on a XY-plane, and two characteristic quantities, one is called compactness of protein structure and another is called probability ratio involving shortest path. The concept of shortest path enables us to reduce the 388 × 388 density matrix to a 2 × 2 one from which the von Neumann entropy reflecting certain quantum coherence feature is naturally defined. We observe the time evolution of average distance and compactness solved from the classical random walk and quantum walk, we also compare the features of the time-dependence of Shannon entropy and von Neumann entropy. All the results not only reveal the fast quantum folding time but also unveil the existence of quantum intelligence hidden behind in choosing protein folding pathways.
Protein folding has been one of the most difficult problems for over a half-century,The random thermal motions causing conformational changes that lead energetically downhill towards the native structure, a principle captured in funnel-shaped energy landscapes.Unfolded polypeptides have a wide range of possible conformations. The search problem becomes intractable for classical computers due to the exponential growth of potential conformations with chain length. So far, there is theoretical and experimental evidence that solving such optimization problems using Quantum Computing approaches such as Quantum Annealing, VQE, and QAOA has an advantage. Although Google's DeepMind-AlphaFold has accomplished much,But we can go even further with the quantum approach.Here we show how to predict structure of protein as well as RNA folding using the Variational Quantum Eigensolver with Conditional Value at Risk (CVaR) expectation values for the solution of the problem and for finding the minimum configuration energy and our task is to identify a protein's minimal energy structure. The protein's structure is optimized to reduce energy. Also making sure that all physical constraints are met and encoding the protein folding problem into a qubit operator.
In computational biology, protein folding continues to be a key challenge with important implications for drug design and biological function comprehension. Using both classical and quantum computing methods, this paper provides an in-depth analysis of various possible lattice configurations and how theyare used in protein folding. To assess the protein design, we investigate classic models including the more intricate Miyazawa-Jernigan (MJ) interaction matrix and the hydrophobic-hydrophilic (HP) lattice model. Furthermore, we use integer programming methods to protein folding problems on lattices, addressing their NP-hardness and proving their effectiveness in obtaining optimal solutions. Expanding upon these conventional techniques, we present sophisticated graph neural network (GNN) models, particularly the RG-MPNN, which integrate pharmacophore ideas to predict chemical properties by using task-related representations. In the latter section of our paper, we examine how protein folding simulations on lattice structures could be transformed using quantum algorithms. Compared to classical methods, quantum computational techniques promise to be more accurate and efficient at handling the enormous diversity and complexity of protein folding.
Abstract Predicting a protein’s three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a fitting domain for applying quantum annealing, an algorithmic technique posited to be faster than its classical counterparts. Nevertheless, the utility of quantum annealing is intrinsically contingent upon the spectral gap associated with the Hamiltonian of lattice proteins. This critical dependence introduces a limitation to the efficacy of these techniques, particularly in the context of simulating the intricate folding processes of proteins. In this paper, we address lattice protein folding as a polynomial unconstrained binary optimization problem, devising a hybrid quantum–classical algorithm to determine the minimum energy conformation effectively. Our method is distinguished by its logarithmic scaling with the spectral gap, conferring a significant edge over the conventional quantum annealing algorithms. The present findings indicate that the folding of lattice proteins can be achieved with a resource consumption that is polynomial in the lattice protein length, provided an ansatz state that encodes the target conformation is utilized. We also provide a simple and scalable method for preparing such states and further explore the adaptation of our method for extension to off-lattice protein models. This work paves a new avenue for surmounting complex computational biology problems via the utilization of quantum computers.
Protein folding has been one of the most difficult problems for over a half-century,Therandom thermal motions causing conformational changes that lead energeticallydownhill towards the native structure, a principle captured in funnel-shaped energylandscapes.Unfolded polypeptides have a wide range of possible conformations. Thesearch problem becomes intractable for classical computers due to the exponentialgrowth of potential conformations with chain length. So far, there is theoretical andexperimental evidence that solving such optimization problems using QuantumComputing approaches such as Quantum Annealing, VQE, and QAOA has anadvantage. Although Google's DeepMind-AlphaFold has accomplished much,But wecan go even further with the quantum approach.Here we show how to predict structureof protein as well as RNA folding using the Variational Quantum Eigensolver withConditional Value at Risk (CVaR) expectation values for the solution of the problem andfor finding the minimum configuration energy and our task is to identify a protein'sminimal energy structure. The protein's structure is optimized to reduce energy. Alsomaking sure that all physical constraints are met and encoding the protein foldingproblem into a qubit operator.