QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

Sarkar, Aritra; Al-Ars, Zaid; Almudéver, Carmen G.; Bertels, Koen

doi:10.3390/electronics10192433

Cited by 11 publications

(13 citation statements)

References 32 publications

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“…This approach, called QiBAM, has been proposed by Sarkar et al . [ 140 ]. It basically extends Grover’s search algorithm to allow for errors in the alignment between reads and the reference sequence stored in a quantum memory (QRAM).…”

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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“…With the implementation of unitary decomposition, OpenQL can now be used for any quantum algorithm that uses arbitrary unitary gates. One such algorithm is QiBAM [8], which cannot be implemented without unitary decomposition.…”

Section: Discussionmentioning

confidence: 99%

“…The two algorithms that will be discussed both use a unitary matrix in the process of finding the position of a short read (sequence of a small piece of DNA) on a reference genome. That matrix needs to be decomposed before the algorithm can be run on a quantum accelerator or simulator [8].…”

Section: Motivation For Unitary Decompositionmentioning

confidence: 99%

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Efficient Decomposition of Unitary Matrices in Quantum Circuit Compilers

et al. 2022

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Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For the selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition, which generates O(344n) controlled-not gates for an n-qubit input gate. In addition, we implement optimizations to take advantage of the potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition. Comparing our implementation to Qubiter and the UniversalQCompiler (UQC), we show that our implementation generates circuits that are much shorter than those of Qubiter and not much longer than the UQC. At the same time, it is also up to 10 times as fast as Qubiter and about 500 times as fast as the UQC.

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“…Expected empirical advantages vary greatly. Among the most promising near-term applications are ones that leverage quantum simulation and quantum machine learning techniques, such as quantum neural networks.target applicationexperimental demonstrationhardware devicealgorithm typeclassical complexityexpected advantageprotein folding and conformation simulation [236–240]yesquantum annealerquantum annealingpolynomial; heuristic approximationunknown, up to polynomialmolecular docking simulation [244]noGaussian Boson samplersamplingsuper-polynomialunknown; up to super-polynomial de novo assembly [223,241]yesquantum annealer; universal gate-based quantum devicequantum annealing, optimizationpolynomial; heuristic approximationunknown, up to polynomialsequence alignment [67–69,245,246]nouniversal gate-based quantum deviceoptimizationpolynomial; heuristic approximationpolynomialsequence matching [71,247,248]nouniversal gate-based quantum deviceQML; searchpolynomialup to super-polynomialinference of phylogenetic trees [70]nouniversal gate-based quantum deviceoptimizationsuper-polynomialpolynomialinference of biological networks [233,249,250]yesquantum annealeroptimization…”

Section: Future Prospects In Biology and Medicinementioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is 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 to 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 practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

Cited by 11 publications

References 32 publications

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

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

Efficient Decomposition of Unitary Matrices in Quantum Circuit Compilers

Biology and medicine in the landscape of quantum advantages

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