Coarse-grained lattice protein folding on a quantum annealer

Babej, Tomáš; Ing, Christopher; Fingerhuth, Mark

doi:10.48550/arxiv.1811.00713

Cited by 20 publications

(30 citation statements)

References 18 publications

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“…Even though introducing the distances reciprocals and deci-mal numbers in the MR model limit the number of designable sites in a system to three, this model enables us to use more realistic energy terms in the Hamiltonian of a design problem on quantum computers, i.e., mimicking the coulomb energies. Our approach in implementing the distances reciprocals and decimal numbers could also be employed in the protein folding studies with quantum computers that are currently limited to the lattice models [11][12][13][14].…”

Section: Discussionmentioning

confidence: 99%

“…Unlike conventional approaches, quantum computation techniques are expected to enhance solving the NP class of problems in their exact forms [3]. In recent years, there have been attempts on using quantum computers to solve NP-hard problems in protein studies, mainly focused on protein folding [11][12][13][14]. In these studies, hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) [15] and the Variational Quantum Eigensolver (VQE) [16] are implemented.…”

Section: Introductionmentioning

confidence: 99%

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Gate-based Quantum Computing for Protein Design

Khatami¹,

Mendes²,

Wiebe³

et al. 2022

Preprint

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Protein design is a technique to engineer proteins by modifying their sequence to obtain novel functionalities. In this method, amino acids in the sequence are permutated to find the low energy states satisfying the configuration. However, exploring all possible combinations of amino acids is generally impossible to achieve on conventional computers due to the exponential growth of possibilities with the number of designable sites. Thus, sampling methods are currently used as a conventional approach to address the protein design problems. Recently, quantum computation methods have shown the potential to solve similar types of problems. In the present work, we use the general idea of Grover's algorithm, a pure quantum computation method, to design circuits at the gate-based level and address the protein design problem. In our quantum algorithms, we use custom pair-wise energy tables consisting of eight different amino acids. Also, the distance reciprocals between designable sites are included in calculating energies in the circuits. Due to the noisy state of current quantum computers, we mainly use quantum computer simulators for this study. However, a very simple version of our circuits is implemented on real quantum devices to examine their capabilities to run these algorithms. Our results show that using O( √ N ) iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover's algorithm over classical methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gate-based Quantum Computing for Protein Design

Khatami¹,

Mendes²,

Wiebe³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Babbush et al showed that such a model can be reduced to PUBO, which paves the way to applying quantum annealers and their simulators in the protein folding problem [37]. Although lattice protein folding is a toy-model, it has been shown to predict model protein tertiary structures to remarkable accuracy [51].In what follows, we briefly discuss the idea behind this reduction.…”

Section: A Lattice Protein Foldingmentioning

confidence: 99%

Polynomial unconstrained binary optimisation inspired by optical simulation

Chermoshentsev¹,

Malyshev²,

Tiunov³

et al. 2021

Preprint

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We propose an algorithm inspired by optical coherent Ising machines to solve the problem of polynomial unconstrained binary optimisation (PUBO). We benchmark the proposed algorithm against existing PUBO algorithms on the extended Sherrington-Kirkpatrick model and random third-degree polynomial pseudo-Boolean functions, and observe its superior performance. We also address instances of practically relevant computational problems such as protein folding and electronic structure calculations with problem sizes not accessible to existing quantum annealing devices. In particular, we successfully find the lowest-energy conformation of lattice protein molecules containing up to eleven amino-acids. The application of our algorithm to quantum chemistry sheds light on the shortcomings of approximating the electronic structure problem by a PUBO problem, which, in turn, puts into question the applicability of quantum annealers in this context.

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“…In particular, the protein folding problem -which has been studied classically for decades [1][2][3][4] -has been the subject of a number of quantum studies in recent years [5][6][7][8][9][10]. However, although these works have made important contributions to our understanding of how quantum computers may be applied to this domain, they have either been restricted to lattice-based toy models of proteins or have been based on methods which lack theoretical performance guarantees such as quantum annealing or the quantum approximate optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Antibody loop modelling on a quantum computer

Allcock,

Vangone,

Meyder

et al. 2021

Preprint

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Predicting antibody structure plays a central role in drug development. The structural region responsible for most of the binding and function of an antibody, namely the H3 loop, is also the most variable and hard to predict to atomic accuracy. The desire to facilitate and accelerate the engineering of therapeutic antibodies has led to the development of computational methodologies aimed at antibody and H3 loop structure prediction. However, such approaches can be computationally demanding and time consuming, and still limited in their prediction accuracy. While quantum computing has been recently proposed for protein folding problems, antibody H3 loop modelling is still unexplored.In this paper we discuss the potential of quantum computing for fast and high-accuracy loop modelling with possible direct applications to pharmaceutical research. We propose a framework based on quantum Markov chain Monte Carlo for modelling H3 loops on a fault-tolerant quantum computer, and estimate the resources required for this algorithm to run. Our results indicate that further improvements in both hardware and algorithm design will be necessary for a practical advantage to be realized on a quantum computer.However, beyond loop modelling, our approach is also applicable to more general protein folding problems, and we believe that the end-to-end framework and analysis presented here can serve as a useful starting point for further improvements.

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Coarse-grained lattice protein folding on a quantum annealer

Cited by 20 publications

References 18 publications

Gate-based Quantum Computing for Protein Design

Gate-based Quantum Computing for Protein Design

Polynomial unconstrained binary optimisation inspired by optical simulation

Antibody loop modelling on a quantum computer

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