Quantum finite difference solvers for physics simulation

Chagneau, Anthony; Nathoo, Laëticia; Alloul, Jérémy; Gabriel, Bertrand

doi:10.1049/qtc2.12054

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…These could all provide alternative approaches to the quantum Jacobi method outlined here. Although the name quantum Jacobi method also appears in [40], it in fact is a very distinct protocol that still relies on the HHL subroutine [3], which is a very different framework compared with Schrödingerisation.…”

Section: Discussionmentioning

confidence: 99%

Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra

Jin,

Liu

2024

Proc. R. Soc. A.

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Quantum simulation is capable of simulating certain dynamical systems in continuous time—Schrödinger’s equations being the most direct and well known—more efficiently than classical simulation. Any linear dynamical system can in fact be transformed into a system of Schrödinger’s equations via a method called Schrödingerisation (Jin et al. 2022. Quantum simulation of partial differential equations via Schrödingerisation. ( https://arxiv.org/abs/2212.13969 ) and Jin et al. 2023. Phys. Rev. A 108 , 032603. ( doi:10.1103/PhysRevA.108.032603 )). We show how Schrödingerisation allows quantum simulation to be directly used for the simulation of continuous-time versions of general (explicit) iterative schemes or discrete linear dynamical systems. In particular, we use this new method to solve linear systems of equations and for estimating the maximum eigenvector and eigenvalue of a matrix, respectively. This method is applicable using either discrete-variable quantum systems or on hybrid continuous-variable and discrete-variable quantum systems. This framework provides an interesting alternative to solve linear algebra problems using quantum simulation.

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Section: Discussionmentioning

confidence: 99%

Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra

Jin,

Liu

2024

Proc. R. Soc. A.

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Quantum computing challenges and solutions in software industry—A multivocal literature review

Salam,

Ilyas

2024

IET Quantum Communication

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Quantum computing (QC) hinged upon the bedrock principles of quantum theory and holds promise for reforming a large number of industries. The researcher in this area aims to deliver a comprehensive understanding of the current state of the art and future trajectories of QC. The authors have discovered that most academic studies have concentrated upon dissecting specific aspects of QC. This discernment underscores the exigency of identifying challenges that might impede the seamless integration of QC within the software industry. Moreover, it becomes crucial to ascertain the panoply of solutions/practices required to overcome these barriers. A comprehensive multi‐vocal literature review was performed and culled a total of 49 academic papers for data extraction. A total of 13 challenges encountered by organisations were identified during the adoption of QC. Subsequently, these challenges were examined deeply and determined that five of them are the most critical, these are ‘Lack of quantum specific algorithms, dev and testing methodologies’, ‘Difficult compilation and debugging’, ‘Lack of development tools and technology’, ‘Lack of development guidelines & Quality Assurance Standards’ and ‘Lack of professional expert’, together founding over 30% of occurrences. These challenges from various perspectives were evaluated, including time frame, methodology, geographical region and publication platform. To address these barriers and implement the QC in software industry effectively, a total of 53 practices/solutions. This research aims to share valuable knowledge to simplify and amplify quantum application development.

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Quantum algorithm for bioinformatics to compute the similarity between proteins

Chagneau,

Massaoudi,

Derbali

et al. 2024

IET Quantum Communication

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Drug discovery has become a main challenge in the society, following the COVID‐19 pandemic. However, pharmaceutical companies are already using computing to accelerate drug discovery and are increasingly interested in quantum computing (QC), with a view to improving the speed of development process for new drugs. The authors propose a quantum method for generating random sequences based on occurrence in a protein database and quantum algorithms for calculating a similarity rate between proteins. Both concepts can be used for structure prediction in drug design. The aim is to find the proteins closest to the generated protein and obtain an ordering of these proteins. First, the authors will present the construction of a quantum protein generator that defines a protein, called a test protein. The authors will then describe different methods to compute the similarity's rate between each protein in the database and the test protein or, for a case study, the elafin. The algorithms have been extended or adapted to a quantum formalism for use cases, that is, amino acid sequences, and tested to see the added value of quantum versions. The interest is to observe whether QC can be used in the drug discovery process.

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Quantum finite difference solvers for physics simulation

Cited by 3 publications

References 34 publications

Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra

Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra

Quantum computing challenges and solutions in software industry—A multivocal literature review

Quantum algorithm for bioinformatics to compute the similarity between proteins

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