Genome assembly using quantum and quantum-inspired annealing

Boev, Aleksey S.; Rakitko, Alexander; Usmanov, Sergey R.; Kobzeva, A.N.; Popov, Iaroslav; Ilinsky, Valery; Kiktenko, Evgeniy O.; Fedorov, Aleksey K.

doi:10.1038/s41598-021-88321-5

Cited by 38 publications

(36 citation statements)

References 43 publications

(41 reference statements)

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“…In Boev et al ( 2021 ), the authors developed a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the φ X 174 bacteriophages.…”

Section: Applications With Quantum Computing Algorithmsmentioning

confidence: 99%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

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In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world.

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Section: Applications With Quantum Computing Algorithmsmentioning

confidence: 99%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

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“…The quantum properties of DNA have been proposed for use in sequencing (for example, interpreting electron tunneling current-voltage differences between the four nucleotide bases as a strand of DNA passes through a nanopore [75]), but quantum methods are mainly deployed in sequence reconstruction (aligning and merging reads to reassemble the original genome). Quantum algorithms have been proposed (for both gate-array and quantum annealing machines) to accelerate DNA sequence reconstruction [76] and demonstrated on quantum annealing platforms to reconstruct short sequences (seven nucleotides) [77]. Quantum annealing machines are also used in basic research to assess the binding affinity of gene regulatory proteins to the genome [78].…”

Section: Quantum Genomicsmentioning

confidence: 99%

Quantum Neurobiology

2022

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Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

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“…Given their early development, large qubit counts, and robust connectivity maps, multiple proof of principle demonstrations targeting bioinformatics and computational biology applications have been developed using quantum annealers. These include ranking and classification of transcription factor binding affinities [257], the discovery of biological pathways in cancer from genepatient mutation data [258], cancer subtyping [259], the prediction of amino acid side chains and conformations that stabilize a fixed protein backbone (a key procedure in protein design) [260], various approaches to protein folding [261,262,263,264,265], and two recent approaches for de novo assembly of genomes [248,266].…”

Section: Quantum Annealingmentioning

confidence: 99%

“…In the near term, these refinements could include i) recasting them for NISQ devices using the VQA, QAOA, or QA frameworks and ii) integrating greater biological context. Already, examples of this type of work exist for de novo assembly [248,266], sequence alignment [270], and the inference of biological networks [274,275]. Over the long term, operational advantages may be pursued by optimizing near term approaches and integrating fast quantum algorithm subroutines where possible.…”

Section: Prospects For Bioinformaticsmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier¹,

Sawaya²,

Guerreschi³

et al. 2021

Preprint

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Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

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Genome assembly using quantum and quantum-inspired annealing

Cited by 38 publications

References 43 publications

A review on quantum computing and deep learning algorithms and their applications

A review on quantum computing and deep learning algorithms and their applications

Quantum Neurobiology

Biology and medicine in the landscape of quantum advantages

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