Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment

Prousalis, Konstantinos; Konofaos, N.

doi:10.1038/s41598-019-43697-3

Cited by 22 publications

(24 citation statements)

References 37 publications

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“…Therefore, the number of fully connected logical qubits required for solving a real problem is about 133, for sequences of 50 base pairs to be searched in the human genome (3 × 10 9 base pairs). 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 ]. It is based on dot matrix, a simple structure for comparing two sequences point by point.…”

Section: Genome Assembly and Pattern Matchingmentioning

confidence: 99%

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

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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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.

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Section: Genome Assembly and Pattern Matchingmentioning

confidence: 99%

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

Marchetti

Nifosı̀

Martelli

et al. 2022

Briefings in Bioinformatics

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“…Examples of quantum algorithms in this class include ones for constraint satisfaction [85] and combinatorial optimization [65,66,86]. Notably, algorithms in this class were among the first to target applications specific to biology and medicine, including sequence alignment [87,88,89] and the inference of phylogenetic trees [90]. Sequence alignment, in particular, represents a crucial computational primitive for many tasks in bioinformatics and computational biology.…”

Section: Classifying Theoretical Advantagesmentioning

confidence: 99%

“…A small number of quantum algorithms for problems in bioinformatics have been proposed (Table 3). These include theoretical algorithms developed for FTQC devices that target NP-hard prob-lems, such as sequence alignment [87,88,89] and the inference of phylogenetic trees [90], which leverage amplitude amplification and quantum walks [409]. To be made practical, these theoretical quantum algorithms are expected to require both significant refinement and effort in translation.…”

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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“…Using quantum Fourier transform, Schutzhold in 2003 devised another protocol for pattern recognition which demonstrated exponential speed-up over its classical analogue [20]. A number of follow-up works attempted to improve the above protocols or presented similar algorithms inspired from them [21][22][23]. Except these, quantum pattern recognition protocols based on the framework of classical Hopfield neural network [24], the hidden shift problem [25], pixel gradient calculation [26], Grover's algorithm [27][28][29] has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Experimental quantum pattern recognition in IBMQ and diamond NVs

Das¹,

Zhang²,

Martina³

et al. 2022

Preprint

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One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a twoqubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when simulated on real IBMQ processors. As test images, we use binary images with simple patterns, greyscale MNIST numbers and MNIST fashion images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy centre (NVs) in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

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Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment

Cited by 22 publications

References 37 publications

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

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

Biology and medicine in the landscape of quantum advantages

Experimental quantum pattern recognition in IBMQ and diamond NVs

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