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Cited by 47Quantum Computing Algorithms and ArchitectureDNA and Biological ComputingComputational Drug Discovery Methods

Quantum computing algorithms: getting closer to critical problems in computational biology

Open access
Marchetti, Laura, Nifosì, Riccardo, Martelli, Pier Luigi, Da Pozzo, Eleonora, Cappello, Valentina, Banterle, Francesco, Trincavelli, Maria Letizia, Martini, Claudia +1 more·2022·Briefings in Bioinformatics
DOI 10.1093/bib/bbac437PMC PMC9677474
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Abstract

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

AI snapshot

Written from the paper’s full text.

TL;DR

This review surveys quantum computing algorithms proposed for computational biology, focusing on multi-scale molecular modelling and genomic analysis. It argues hybrid classical/quantum approaches are closest to practical use, while current hardware limits—qubit counts and decoherence—prevent real-world application.

Key findings

  • Quantum chemistry: VQE and QPE are the main digital algorithms; VQE experiments have been performed for H2, LiH, H2O, up to H12 and diazene on 2–12 qubits, and QPE was applied to H2O on a four-qubit quantum simulator. A VQE-based ligand–protein interaction method for KDM5A used a classical simulation of a 16-qubit quantum computation.
  • Genomics: Grover-based alignment gives an O(√N) speedup; QiBAM requires about 133 logical qubits for 50-bp sequences searched in the human genome (3 × 10⁹ bp); a dot-matrix/quantum Fourier transform method has O(log₂(NM)) time complexity. De novo assembly QUBO/annealing approaches exist, but QuASeR on a D-Wave 2000Q annealer could successfully assemble only 9 reads because of embedding overhead.
  • Protein folding/design: quantum annealing has been applied to lattice models; a proof-of-principle design produced a 32-amino-acid peptide on a D-Wave 2000Q system; gate-based protein folding requires qubits scaling quadratically with the number of amino acids; protein design space is 20^N for N amino acids.
  • Hybrid classical/quantum approaches in machine learning, quantum chemistry, and genomic data analysis are identified as closest to realistic application; quantum advantage is estimated to require around 10⁶–10⁷ physical qubits.

Methods

  • Narrative review/commentary, not primary research; it synthesizes recently developed QC algorithms for biocomputing with particular focus on multi-scale modelling and genomic analyses.
  • No new dataset or sample; evidence is drawn from cited proof-of-concept studies, simulations, and small hardware demonstrations.
  • Main analysis is qualitative comparison of algorithmic mappings, complexity claims, and hardware requirements.

Limitations

  • Stated: current quantum machines have limited qubits (O(10²), O(10³) for annealers), limited circuit size O(10²) before decoherence and other faults ruin computation, and QPE requires fault-tolerant hardware; quantum advantage is estimated at around 10⁶–10⁷ physical qubits.
  • Stated: VQE is heuristic, gives an upper bound rather than the exact result, requires more Hamiltonian-averaging subcycles as molecule size grows, and its parameter training is NP-hard; real genomic problems exceed current quantum technology.
  • Evident: as a narrative review, it does not perform a systematic search or independent benchmarking, so comparisons rely on cited studies and may not be exhaustive.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1093/bib/bbac437

Cites

153 of 153 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 105 cited sources checked · 0 supported · 0 flagged

  • Findingcitation

    A proof-of-principle application led to the design of a 32-amino-acid long peptide on a D-Wave 2000Q system.

    “A proof-of-principle application led to the design of a 32-amino-acid long peptide on a D-Wave 2000Q system [79].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [79] Mulligan VK, Melo H, Merritt HI, et al. Designing peptides on a quantum computer. bioRxiv preprint, bioRxiv 2019; 752485.

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  • Findingcitation

    The hybrid transfer learning strategy was demonstrated on Rigetti and IBM machines with success.

    “Even though this reasonable hybrid strategy was demonstrated on Rigetti and IBM machines with success [64]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [64] Mari A, Bromley TR, Izaac J, et al. Transfer learning in hybrid classical-quantum neural networks. Quantum 2020; 4: 340. source

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  • Interpretationcitation

    Using the entangled state of many standard qubits for fault-tolerant qubits would largely increase the required number of qubits.

    “but that would largely increase the required qubits”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [52] Gottesman D. An introduction to quantum error correction and fault-tolerant quantum computation. Arxiv preprint arXiv:0904.2557. 2009.

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  • Interpretationreasoning

    The problems of limited qubit number and gate fidelity can be solved only in a long-term perspective.

    “Such problems can be solved only in a long-term perspective”
    Quote in the paperNot a substantial claim
  • Interpretationcitation

    Fault-tolerant qubits would also increase the time requested for each elementary operation.

    “one should also consider the increased time requested for each elementary operation [53, 54]”
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    • cites [53] Babbush R, McClean J, Newman M, et al. Focus beyond quadratic speedups for error-corrected quantum advantage. arXiv:201104149 2020.

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    • cites [54] Allcock J, Vangone A, Meyder A, et al. The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer. Front Drug Discov 2022; 2: 908870. source

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  • Classified as probably not a substantial claim. 41
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    Premisecitation

    The near-term perspective is Noisy Intermediate-Scale Quantum computing, in which quantum hardware is used only to run short depth circuits as part of more complex algorithms involving classical hardware, i.e. as an accelerator.

    “The near term perspective is Noisy Intermediate-Scale Quantum (NISQ) computing [55]: the quantum hardware is used only to run short depth circuits as part of more complex algorithms involving also classical hardware, i.e. the quantum computer is used as an accelerator.”
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    • cites [55] Preskill J. Quantum computing in the NISQ era and beyond. Quantum 2018; 2: 79. source

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    Premisenone given

    Quantum computing approaches have been proposed so far for many of the computational problems listed in the review.

    “Noteworthy, QC approaches have been proposed so far for many of the computational problems listed above.”
    Quote in the paperNot a substantial claim

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    Interpretationnone given

    Quantum computing approaches hold the promise to resolve, speed up or refine the analysis of a wide range of computational problems in biocomputing.

    “Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Multi-scale modelling and genomic analyses have been adequately mapped onto quantum architectures.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Machine learning has been demonstrated to improve the performance of most of the above-mentioned computational tasks.

    “indeed machine learning has been demonstrated to improve the performance of most of the above-mentioned computational tasks [57]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [57] Greener JG, Kandathil SM, Moffat L, et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022; 23: 40– 55. 34518686

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    Premisecitation

    Quantum approaches have been proposed to improve the classification process, either by development of quantum neural networks or by development of a quantum support.

    “As concerns quantum approaches, they have been proposed to improve the classification process [58, 59], either by development of quantum neural networks [60–62] or, more recently, by development of a quantum support”
    Quote in the paperNot a substantial claim0 of 5 sources checked
    • cites [58] Nath RK, Thapliyal H, Humble TS. A review of machine learning classification using quantum annealing for real-world applications. SN Comput Sci 2021; 2: 365.

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    • cites [59] Li RY, Di Felice R, Rohs R, et al. Quantum annealing versus classical machine learning applied to a simplified computational biology problem. Npj Quantum Inf. 2018; 4: 14. 29652405 PMC5891835 source

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    • cites [60] Kerenidis I, Landman J, Prakash A. Quantum algorithms for deep convolutional neural networks. Arxiv preprint arXiv:1911.01117. 2019.

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    • cites [61] Cong I, Choi S, Lukin MD. Quantum convolutional neural networks. Nat Phys 2019; 15: 1273– 8. source

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    • cites [62] Heidari N, Olgiati S, Meloni D, et al. A quantum-enhanced precision medicine application to support data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis: development and preliminary validation of precisionKNEE_QNN. MedRXiv preprint MedRXiv2021.12.13.21267704. 2022. PMC10782883 38213940

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    Premisecitation

    Present technologies for building qubits are based on superconductors, cold atoms, trapped ions and Rydberg atoms and presently scale up to O(10^2) qubits, or O(10^3) for annealers.

    “Present technologies for building qubits are based on superconductors, cold atoms, trapped ions and Rydberg atoms (see Refs [48–51] for recent reviews) and presently scale up to O(102) qubits (O(103) for annealers).”
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    • cites [48] Polini M, Giazotto F, Fong KC, et al. Materials and devices for fundamental quantum science and quantum technologies. arXiv preprint arXiv:2201.09260. 2022.

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    • cites [49] Schäfer F, Fukuhara T, Sugawa S, et al. Tools for quantum simulation with ultracold atoms in optical lattices. Nat. Rev. Phys. 2020; 2: 411– 25.

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    • cites [50] Monroe C, Campbell WC, Duan L-M, et al. Programmable quantum simulations of spin systems with trapped ions. Rev Mod Phys 2021; 93: 025001.

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    • cites [51] Adams CS, Pritchard JD, Shaffer JP. Rydberg atom quantum technologies. J Phys B At Mol Opt Phys 2020; 53: 012002.

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    Interpretationcitation

    Fault-tolerant qubits could be based on the entangled state of many standard qubits.

    “fault-tolerant qubits could be based on the entangled state of many standard qubits [52]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [52] Gottesman D. An introduction to quantum error correction and fault-tolerant quantum computation. Arxiv preprint arXiv:0904.2557. 2009.

      Not checked in the free run.

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    Interpretationreasoning

    Machine learning holds great potential for the analysis of complex data sets such as biomedical imaging data.

    “In particular, machine learning holds great potential for the analysis of complex data sets such as biomedical imaging data”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Hybrid classical/quantum approaches could be the closest to being realistically applied in biocomputation.

    “which could be the closest to be realistically applied in biocomputation”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    VQE adopts a hybrid approach combining quantum and classical computation.

    “VQE [99] (Figure 3A) adopts a hybrid approach combining quantum and classical computation.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [99] Peruzzo A, McClean J, Shadbolt P, et al. A variational eigenvalue solver on a photonic quantum processor. Nat Commun 2014; 5: 4213. 25055053 PMC4124861 source

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    Premisenone given

    The choice of the ansatz circuit determines which subspace of the general Hilbert space can be spanned by the variational procedure.

    “The choice of the ansatz circuit determines which subspace of the general Hilbert space can be spanned by the variational procedure.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    The terms needed in the CASSCF FCI expansion easily reach the limits of manageability by classical computational resources, with the current record being 22 electrons in 22 molecular orbitals, leading to 5 × 10^11 terms.

    “Notwithstanding this restriction, the terms needed in the CASSCF FCI expansion easily reach the limits of manageability by classical computational resources, the current record being at 22 electrons in 22 molecular orbitals, leading to 5 × 1011 terms [94].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [94] Vogiatzis KD, Ma D, Olsen J, et al. Pushing configuration-interaction to the limit: towards massively parallel MCSCF calculations. J Chem Phys 2017; 147: 184111. 29141437 source

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    Premisecitation

    The full configuration interaction (FCI) approach gives the most accurate wavefunctions within the chosen basis set of single-electron states.

    “This is the so called ‘full configuration interaction’ (FCI) approach [93], which gives the most accurate wavefunctions within the chosen basis set of single-electron states.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [93] Helgaker T, Jørgensen P, Olsen J. Molecular Electronic-Structure Theory. New York: Wiley, 2000. source

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    Interpretationreasoning

    The main advantage of the hybrid transfer learning approach is that the quantum machine does not need to handle images, which may require many qubits, and the classification achieves high accuracy.

    “The main advantage of such an approach is that the quantum machine does not need to handle images, which may require many qubits, and the classification achieves high accuracy.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The electronic structure problem is considered to be among the first practical applications of quantum computing.

    “Indeed, the electronic structure problem is considered to be among the first practical applications of QC [91].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [91] Aspuru-Guzik A, Dutoi AD, Love PJ, et al. Simulated quantum computation of molecular energies. Science 2005; 309: 1704– 7. 16151006 source

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    Premisecitation

    QPE is a central subroutine of many quantum algorithms and can be applied to the electronic structure problem rather straightforwardly.

    “QPE is a central subroutine of many quantum algorithms [84, 113] and can be applied to the electronic structure problem rather straightforwardly.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [84] Abrams DS, Lloyd S. Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors. Phys Rev Lett 1999; 83: 5162– 5. source

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    • cites [113] AYU K. Quantum measurements and the abelian stabilizer problem. arXiv:quant-ph/9511026 1995.

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    Premisecitation

    Gate-based quantum algorithms are being proposed to address biopolymer conformations, including a hybrid approach combining quantum Monte Carlo and machine learning for protein structure prediction, another quantum Monte Carlo approach to antibody loop modelling, and protein design exploiting Grover's algorithm.

    “Gate-based quantum algorithms are also being proposed to address biopolymer conformations, including a hybrid approach combining quantum Monte Carlo and machine learning for protein structure prediction [80], another quantum Monte Carlo approach to antibody loop modelling [54] and  protein design exploiting Grover’s algorithm [81].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [80] Casares PAM, Campos R, Martin-Delgado MA. QFold: quantum walks and deep learning to solve protein folding. Quantum Sci Technol 2022; 7: 025013.

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    • cites [54] Allcock J, Vangone A, Meyder A, et al. The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer. Front Drug Discov 2022; 2: 908870.

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    • cites [81] Khatami MH, Mendes UC, Wiebe N, et al. Gate-based quantum computing for protein design. arXiv preprint arXiv:2201.12459. 2022. PMC10124842 37043517

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    Interpretationnone given

    These molecules are very well within the reach of accurate classical algorithms.

    “These molecules are very well within the reach of accurate classical algorithms”
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    Premisecitation

    A protein of N amino acids can adopt at least 3N possible 3D structures.

    “One can roughly estimate that a protein of N amino acids can adopt up at least 3N possible 3D structures [69].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [69] Englander SW, Mayne L. The nature of protein folding pathways. Proc Natl Acad Sci USA 2014; 111: 15873– 80. 25326421 PMC4234557

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    Premisecitation

    Robert et al. report a quantum gate-based approach to protein folding on a lattice using a modified version of the VQE algorithm and requiring a number of qubits that scales quadratically with the number of amino acids.

    “Robert et al. [73] report a quantum gate-based approach to protein folding on a lattice, using a modified version of the VQE algorithm (discussed in the next section) and requiring a number of qubits that scales quadratically with the number of amino acids.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [73] Robert A, Barkoutsos PK, Woerner S, et al. Resource-efficient quantum algorithm for protein folding. Npj Quantum Inf. 2021; 7(38).

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    Premisecitation

    The method is applied to lysine-specific demethylase 5A (KDM5A) using a classical computer simulation of a 16-qubit quantum computation.

    “The method is applied to lysine-specific demethylase 5A (KDM5A) using a classical computer simulation of a 16-qubit quantum computation.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [105] Malone FD, Parrish RM, Welden AR, et al. Towards the simulation of large scale protein–ligand interactions on NISQ-era quantum computers. Chem Sci 2022; 13: 3094– 108. 35414867 PMC8926290

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    Premisecitation

    In the Jordan-Wigner mapping, the number of gates needed to realize the basic creation and annihilation operators increases linearly with the number of electrons, whereas the Bravyi-Kitaev encoding leads to logarithmic scaling.

    “In the case of the Jordan–Wigner mapping this means that the number of gates needed to realize the basic creation and annihilation operators increases linearly with the number of electrons in the system. Other encodings, such as the one devised by Bravyi–Kitaev [98], lead instead to a more convenient logarithmic scaling.”
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    • cites [97] Jordan P, Wigner E. Über das Paulische Äquivalenzverbot. Z Für Phys 1928; 47: 631– 51. source

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    • cites [98] Bravyi SB, AYU K. Fermionic quantum computation. Ann Phys 2002; 298: 210– 26. source

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    Premisecitation

    The protein design space is bigger than the protein folding one, as 20N configurations are possible when designing a protein of N amino acids.

    “however, the protein design space is even bigger than the protein folding one, as 20N configurations are possible when designing a protein of N amino acids [78].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [78] Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13: 587– 604. 29695210 PMC6002944

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    Premisenone given

    Digital quantum computation approaches for the electronic structure problem focus on two families of algorithms: the variational quantum eigensolver (VQE) and quantum phase estimation (QPE).

    “Broadly speaking, the ‘digital’ quantum computation approaches for the electronic structure problem focus on two families of algorithms: the variational quantum eigensolver (VQE) and quantum phase estimation (QPE).”
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    Premisenone given

    To date and to the authors' knowledge, no quantum version has been proposed for de novo assembly based on De Bruijn graphs.

    “To date and to our knowledge, no quantum version has been proposed for the de novo assembly based on the De Brujin graphs.”
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    Premisecitation

    Different algorithms have been introduced through years to improve the solution of the string matching problem.

    “Different algorithms have been introduced through years to improve the solution of the string matching problem [137–139].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [137] Ramesh H, Vinay V. String matching in O(n+m) quantum time. J Discrete Algorithms 2003; 1: 103– 10. source

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    • cites [138] Montanaro A. Quantum pattern matching fast on average. Algorithmica 2017; 77: 16– 39. source

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    • cites [139] Niroula P, Nam Y. A quantum algorithm for string matching. Npj Quantum Inf 2021; 7: 37.

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    Premisecitation

    In these systems, the electronic ground state may be accurately described by classical-computation methods, but to address the interaction with light one needs to simulate also excited states, which are characterized by multireference wavefunctions.

    “In these systems, the electronic ground state may be accurately described by classical-computation methods, but to address the interaction with light one needs to simulate also excited states, which are characterized by multireference wavefunctions [121].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [121] Bauman NP, Liu H, Bylaska EJ, et al. Toward quantum computing for high-energy excited states in molecular systems: quantum phase estimations of core-level states. J Chem Theory Comput 2021; 17: 201– 10. 33332965

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    Premisecitation

    Another solution based on the Quantum Approximate Optimization Algorithm (QAOA) is proposed.

    “In this work, another solution is proposed based on the Quantum Approximate Optimization Algorithm (QAOA, [146]), which is an instance of the VQE algorithm (discussed in the previous section and in Figure 3B) with the following prescription for the ansatz circuit.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [146] Farhi E, Goldstone J, Gutmann S. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 2014.

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    Premisecitation

    This approach, called QiBAM, has been proposed by Sarkar et al.

    “This approach, called QiBAM, has been proposed by Sarkar et al. [140].”
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    • cites [140] Sarkar A, Al-Ars Z, Almudever CG, et al. QiBAM: approximate sub-string index search on quantum accelerators applied to DNA read alignment. Electronics 2021; 10: 2433.

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    Premisecitation

    Recently, new techniques have been developed that generate long multikilobase (i.e. thousands of nucleotides) reads.

    “Recently, new techniques have been developed that generate long multikilobase (i.e. thousands of nucleotides) reads [133].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [133] McCombie WR, McPherson JD, Mardis ER. Next-generation sequencing technologies. Cold Spring Harb Perspect Med 2019; 9: a036798. 30478097 PMC6824406

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    Premisecitation

    The QPE method has been recently applied to the electronic states of H2O on a four-qubit quantum simulator.

    “As a proof of principle, the QPE method has been recently applied to the electronic states of H2O on a four-qubit quantum simulator [114].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [114] Li Z, Liu X, Wang H, et al. Quantum simulation of resonant transitions for solving the eigenproblem of an effective water Hamiltonian. Phys Rev Lett 2019; 122: 090504. 30932514

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    Premisecitation

    These are strongly correlated/multireference systems that are not-and are not projected to be-at reach for classical algorithms.

    “These are strongly correlated/multireference systems that are not—and are not projected to be—at reach for classical algorithms”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [89] McArdle S, Endo S, Aspuru-Guzik A, et al. Quantum computational chemistry. Rev Mod Phys 2020; 92: 015003. source

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    Premisecitation

    A similar, QUBO-based approach is described in QuASer [145] and proved correct on a D-Wave 2000Q annealer.

    “A similar, QUBO-based approach is described in QuASer [145], and proved correct on a D-Wave 2000Q annealer.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [145] Sarkar A, Al-Ars Z, Bertels K. QuASeR: quantum accelerated de novo DNA sequence reconstruction. PloS One 2021; 16: e0249850. 33844699 PMC8041170

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    Premisecitation

    Two basic classical approaches have been developed, both based on graph representation: the overlap-layout consensus (OLC) and the de Brujin graph approach.

    “Two basic classical approaches have been developed, both based on graph representation: the overlap-layout consensus (OLC) and the de Brujin graph approach [135].”
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    • cites [135] Sohn J-I, Nam J-W. The present and future of de novo whole-genome assembly. Brief Bioinform 2018; 19: 23– 40. 27742661

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    Interpretationcitation

    Such an amount of data poses significant computational challenges, in particular when considering that NGS throughput exponentially increases over the years, and the reduction of sequencing costs per genome outweighs that of the costs of computational power, as described by the Moore's law.

    “Such an amount of data poses significant computational challenges, in particular when considering that NGS throughput exponentially increases over the years, and the reduction of sequencing costs per genome outweighs that of the costs of computational power, as described by the Moore’s law [132].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [132] Lightbody G, Haberland V, Browne F, et al. Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application. Brief Bioinform 2019; 20: 1795– 811. 30084865 PMC6917217 source

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    Premisecitation

    It is based on dot matrix, a simple structure for comparing two sequences point by point.

    “It is based on dot matrix, a simple structure for comparing two sequences point by point.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [141] Prousalis K, Konofaos N. Α quantum pattern recognition method for improving pairwise sequence alignment. Sci Rep 2019; 9: 7226. 31076611 PMC6510764 source

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    To date, both methods have been tested only on reduced problems using simulators, showing good performance in both producing the correct alignment and calling the variations.

    “To date, both methods have been tested only on reduced problems using simulators showing good performance in both producing the correct alignment and calling the variations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The most efficient computational algorithms to date exploit the Ferragina-Manzini string index based on the Burrows-Wheeler transform.

    “The most efficient computational algorithms to date exploit the Ferragina–Manzini string index based on the Burrows–Wheeler transform (see [134] for a detailed survey of all short mapping read methods).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [134] Canzar S, Salzberg SL. Short read mapping: an algorithmic tour. Proc IEEE Inst Electr Electron Eng 2017; 105: 436– 58. 28502990 PMC5425171

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Longest diagonals patterns in the matrix, possibly not perfectly shaped owing to mismatches and short insertions/deletions, highlight the regions of highest similarity and can be detected with a quantum pattern recognition scheme based on quantum Fourier transform, following the algorithm presented by Schützhold.

    “Longest diagonals patterns in the matrix, possibly not perfectly shaped owing to mismatches and short insertions/deletions, highlight the regions of highest similarity and can be detected with a quantum pattern recognition scheme based on quantum Fourier transform, following the algorithm presented by Schützhold [142].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [142] Schützhold R. Pattern recognition on a quantum computer. Phys Rev A 2003; 67: 062311.

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Nitrogenases activity depends on a cofactor made of an unusual cluster of sulphur, oxygen and transition metals (Fe, Mo), and its very complex electronic structure prevents a detailed understanding of this important enzymatic reaction.

    “Nitrogenases activity depends on a cofactor made of an unusual cluster of sulphur, oxygen and transition metals (Fe, Mo), and its very complex electronic structure prevents a detailed understanding of this important enzymatic reaction.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [115] Wiig JA, Rebelein JG, Hu Y. Nitrogenase Complex. eLS, John Wiley & Sons, Ltd, 2014.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    One exemplary problem is the structure-function relationship of nitrogenase enzymes, a family of metalloenzymes that catalyse the reduction of dinitrogen (N2) to ammonia (NH3) in the global nitrogen cycle.

    “One exemplary problem that has received much attention in this context is the structure–function relationship of nitrogenase enzymes, a family of metalloenzymes that catalyse the reduction of dinitrogen (N2) to ammonia (NH3) in the global nitrogen cycle [115].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [115] Wiig JA, Rebelein JG, Hu Y. Nitrogenase Complex. eLS, John Wiley & Sons, Ltd, 2014.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    QAOA is an instance of the VQE algorithm.

    “In this work, another solution is proposed based on the Quantum Approximate Optimization Algorithm (QAOA, [146]), which is an instance of the VQE algorithm (discussed in the previous section and in Figure 3B) with the following prescription for the ansatz circuit.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [146] Farhi E, Goldstone J, Gutmann S. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 2014.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A second approach for detecting local alignments between reads and reference sequence, or a slice of it, has been described by Prousalis and Kofonaus.

    “A second approach for detecting local alignments between reads and reference sequence, or a slice of it, has been described by Prousalis and Kofonaus [141].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [141] Prousalis K, Konofaos N. Α quantum pattern recognition method for improving pairwise sequence alignment. Sci Rep 2019; 9: 7226. 31076611 PMC6510764 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Early work has shown the applicability of the Grover's search algorithm to the problem of biological sequence alignment and has proposed a modified algorithm able to tackle the problem of repeated sequences and non-exact matches.

    “Early work has shown the applicability of the Grover’s search algorithm to the problem of biological sequence alignment and has proposed a modified algorithm able to tackle the problem of repeated sequences and non-exact matches [136].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [136] Hollenberg LCL. Fast quantum search algorithms in protein sequence comparisons: quantum bioinformatics. Phys Rev E 2000; 62: 7532– 5. 11102126 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Proton-coupled electron transfer and photoisomerization are often involved in enzymatic and light-induced reactions.

    “Proton-coupled electron transfer [122] and photoisomerization [123] are often involved in enzymatic and light-induced reactions.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [122] Weinberg DR, Gagliardi CJ, Hull JF, et al. Proton-coupled electron transfer. Chem Rev 2012; 112: 4016– 93. 22702235

      Not checked in the free run.

    • cites [123] Gozem S, Luk HL, Schapiro I, et al. Theory and simulation of the ultrafast double-bond isomerization of biological chromophores. Chem Rev 2017; 117: 13502– 65. 29083892

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    These include carotenoids and chlorophyll in the light harvesting complex, the retinal in the vision process as well as infrared fluorescent proteins.

    “These include carotenoids and chlorophyll in the light harvesting complex [118], the retinal in the vision process [119] as well as infrared fluorescent proteins [120].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [118] Segatta F, Cupellini L, Garavelli M, et al. Quantum chemical modeling of the photoinduced activity of multichromophoric biosystems: focus review. Chem Rev 2019; 119: 9361– 80. 31276384 PMC6716121

      Not checked in the free run.

    • cites [119] Hahn S, Stock G. Quantum-mechanical modeling of the femtosecond isomerization in rhodopsin. J Phys Chem B 2000; 104: 1146– 9.

      Not checked in the free run.

    • cites [120] Karasev MM, Stepanenko OV, Rumyantsev KA, et al. Near-infrared fluorescent proteins and their applications. Biochemistry 2019; 84: 32– 50. 31213194

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Similar QC approaches have been adopted for tackling other problems related to the comparison and overlap of sequences, such as multiple sequence alignments.

    “We also underline that similar QC approaches have been adopted for tackling other problems related to the comparison and overlap of sequences, such as multiple sequence alignments.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [95] Cordier BA, NPD S, Guerreschi GG, et al. Biology and medicine in the landscape of quantum advantages. arXiv preprint arXiv:2112.00760. 2021. PMC9709576 36448288

      Not checked in the free run.

    • cites [147] Ali S, Abbadeni N, Batouche M. Eds Multidisciplinary computational intelligence techniques: applications in business, engineering, and medicine. IGI Global 2012.

      Not checked in the free run.

    • cites [148] Layeb A, Meshoul S, Batouche M. Multiple sequence alignment by quantum genetic algorithm. Proceedings 20th IEEE International Parallel and Distributed Processing Symposium. IEEE Computer Society, 2006; 8.

      Not checked in the free run.

    • cites [149] Huo H, Xie Q, Shen X, et al. A probabilistic coding based quantum genetic algorithm for multiple sequence alignment. Comput Syst Bioinforma 2008; 15– 26. 19642265

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisenone given

    There is a lag between the ability to generate and the ability to analyse big data in computational biology.

    “Despite the huge progress in computational biology and bioinformatics in the last 30 years, to date many challenges remain: there is a lag between the ability to generate and that to analyse big data; furthermore, the simulation of the many parameters that recapitulate complex biomolecules or assemblies is limited by computational cost.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    The simulation of the many parameters that recapitulate complex biomolecules or assemblies is limited by computational cost.

    “Despite the huge progress in computational biology and bioinformatics in the last 30 years, to date many challenges remain: there is a lag between the ability to generate and that to analyse big data; furthermore, the simulation of the many parameters that recapitulate complex biomolecules or assemblies is limited by computational cost.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Quantum computing holds the promise to resolve, speed up or refine the analysis of a wide range of computational biology problems.

    “Quantum computing (QC) holds the promise to resolve, speed up or refine the analysis of a wide range of computational biology problems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationnone given

    Hybrid classical-quantum approaches in machine learning, multi-scale modelling and genomic data analysis look promising and may soon approach real applications.

    “At the moment, we have identified here some hybrid classical-quantum approaches in machine learning, multi-scale modelling and genomic data analysis, which look promising and may soon approach to real applications.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    QC is still an emerging technology at the early stages of development.

    “QC is still an emerging technology at the early stages of development but unavoidably raises interest on whether and how it could overcome these limitations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    QC matches the complexity of many real quantum systems.

    “As suggested by R. Feynman, QC matches the complexity of many real quantum systems; hence, it could solve various computational problems, not presently tractable by classical means, by adopting the same computational paradigm used by nature itself.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites R. Feynman

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    More development in hardware capacity and better strategies to model complex biomolecular systems will be required before QC can be used to solve real-world problems.

    “Surely, more development in terms of hardware capacity and better strategies to model complex biomolecular systems will be required, before QC can be used to solve real-world problems.”
    • Also stated in: § Key Points · plain
      “Before QC can be used to solve real-world problems, further development in terms of hardware capacity and better strategies to model complex biomolecular systems will be required.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Hybrid classical-quantum approaches may be the closest to real applications in machine learning, quantum chemistry and genomic data analysis.

    “Hybrid classical-quantum approaches may be the closest to real applications in machine learning, quantum chemistry and genomic data analysis.”
    Quote in the paperNot a substantial claim
55 of 55

  • The paper relies on this without support.

    Multi-scale modelling and genomic analyses have been adequately mapped onto quantum architectures.

    “these problems have been shown to be adequately mapped onto quantum architectures”

  • The paper relies on this without support.

    Quantum mechanical methods are by far the most computationally intensive among multi-scale modelling algorithms.

    “Within this category, the QM methods are, by far, the most computationally intensive.”

  • The paper relies on this without support.

    The number of quantum gates after which decoherence and other faults ruin computation is presently limited to O(10^2).

    “the number of quantum gates (the so-called circuit size) after which decoherence and other faults ruin the computation is presently limited to O(102)”

  • The paper relies on this without support.

    Some processes, such as biochemical reactions, light harvesting in photosynthesis, and the process of vision, can be accurately described only by taking into account the electronic structure of a subsystem.

    “On the one hand, some processes (biochemical reactions, light harvesting in photosynthesis, the process of vision) can be accurately described only taking into account the electronic structure of a subsystem.”

  • The paper relies on this without support.

    States accurately described by a single Slater determinant are relatively easy to simulate, but covalent bond formation and breaking, electronically excited states, and systems containing transition metals require more than one reference for accurate simulation.

    “States that are accurately described by such a single product, or single configuration, or single reference, are relatively easy to simulate. However, there are cases in which this approach fails to even qualitatively capture the behaviour of the system: covalent bond formation and breaking in chemical reactions, electronically excited states, systems containing transition metals and so on require”

  • The paper relies on this without support.

    VQE algorithms are heuristic methods, because, rather than providing the exact result, they give an upper bound to the energy.

    “A critical assessment of the potentiality of VQE algorithms in the long run needs to take into account that they are heuristic methods, because, rather than providing the exact result, they give an upper bound to the energy.”

  • The paper relies on this without support.

    The simulation of these processes is further complicated by the need to take into account the quantum dynamics of the nuclei, as the Born–Oppenheimer approximation ceases to be valid.

    “The simulation of these processes is further complicated by the need to take into account the quantum dynamics of the nuclei, as the Born–Oppenheimer approximation ceases to be valid.”

  • The paper relies on this without support.

    The complex biomolecular structures around these subsystems tune the energy of stationary states (i.e. minimum-energy and transition states), so that their mutual interaction needs to be taken into account.

    “The complex biomolecular structures around these subsystems tune the energy of stationary states (i.e. minimum-energy and transition states), so that their mutual interaction needs to be taken into account.”

  • The paper relies on this without support.

    It is possible in these cases to adopt a hybrid approach, in which a subsystem is treated using a high accuracy method, and the rest is described by less computationally demanding methods.

    “It is possible in these cases to adopt a hybrid approach, in which a subsystem is treated using a high accuracy method, and the rest is described by less computationally demanding methods.”

  • The paper relies on this without support.

    A single experiment on the most modern platforms can generate up to 40 billion short reads (~ 300 nucleotides long).

    “A single experiment on the most modern platforms can generate up to 40 billion short reads (~ 300 nucleotides long).”

  • The paper relies on this without support.

    When a reference genome is not available, a much more difficult problem must be tackled.

    “When a reference genome is not available, a much more difficult problem must be tackled.”

  • The paper relies on this without support.

    The Hamiltonian problem is notoriously computationally complex (NP-hard), so different heuristics are adopted to simplify the graph and reach a reasonable solution.

    “However, the Hamiltonian problem is notoriously computationally complex (NP-hard), so different heuristics are adopted to simplify the graph and reach a reasonable solution.”

  • The paper relies on this without support.

    OLC is a method of choice for datasets consisting of a limited number of long reads.

    “OLC is a method of choice for datasets consisting of a limited number of long reads.”

  • The paper relies on this without support.

    When the number of reads increases, the pairwise comparison of all reads to search for overlaps remains a computational bottleneck.

    “However, when the number of reads increases, the pairwise comparison of all reads to search for overlaps remains a computational bottleneck.”

  • The paper relies on this without support.

    A more efficient solution is represented by de Brujin graphs.

    “A more efficient solution is represented by de Brujin graphs.”

  • The paper relies on this without support.

    Experimental data always contain sequencing errors and coverage unevenness that make the real graph non-Eulerian, and heuristics must be applied to efficiently solve the problem.

    “However, experimental data always contain sequencing errors and coverage unevenness that make the real graph non-Eulerian, and heuristics must be applied to efficiently solve the problem.”

  • The paper relies on this without support.

    In last years, a few proof-of-principle solutions based on QC have been proposed to improve the computational efficiency of algorithms for NGS problems.

    “In last years, a few proof-of-principle solutions based on QC have been proposed to improve the computational efficiency of algorithms for NGS problems.”

  • The paper relies on this without support.

    In the case of resequencing, quantum algorithms aim at finding approximate pattern matching and two solutions have been proposed so far.

    “In the case of resequencing, quantum algorithms aim at finding approximate pattern matching and two solutions have been proposed so far.”

  • The paper relies on this without support.

    Although all of them can be in principle applied to the problem of resequencing, only one algorithm has been explicitly developed to this goal.

    “Although all of them can be in principle applied to the problem of resequencing, only one algorithm has been explicitly developed to this goal.”
19 of 19

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Same arXiv identifier assigned to two different papers

    Minor

    References 68 and 146 assign arXiv:1411.4028 to papers with different titles, authors and publication years. The same unique identifier cannot identify both works.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Seeberg, Essentials of Glycobiology [Internet], 2015
  2. Yıldız, 2020, Computational Biology and Chemistry [Internet]
  3. Krizhevsky, 2012, NIPS12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1097
  4. Sohn, 2018, The present and future of de novo whole-genome assembly, Brief Bioinform, 19, 23

Cited by

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Authors
Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M.
Affiliations
  • University of Pisa (IT)
  • NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore , P.zza San Silvestro 12, 56127 Pisa Italy
  • University of Bologna (IT)
  • University of Pisa, Department of Pharmacy , via Bonanno 6, 56126 Pisa Italy
  • Italian Institute of Technology (IT)
  • Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
Corresponding author
  • Martelli, Pier Luigi
  • Martini, Claudia
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-10-11
Volume
23
Issue
6
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbac437
PMC
PMC9677474
OpenAlex
W4304587339
Cited by
47
References
153
Citations by year
2026: 10 · 2025: 14 · 2024: 13 · 2023: 9 · 2022: 1
FWCI
0.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc)
  • ISTI Open Portal (submittedVersion, other-oa)
Funders
University of Pisa (PRA 2020-2021 92)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA and Biological Computing
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
quantum algorithms, biocomputing, multiscale modeling, genome analysis, hybrid quantum-classical algorithms, protein structure prediction, omics data, decoherence, qubit requirements
MeSH terms
Algorithms, Computing Methodologies, Quantum Theory, Computational Biology, Genomics

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