Models in quantum computing: a systematic review

Nimbe, Peter; Weyori, Benjamin Asubam; Adekoya, Adebayo Felix

doi:10.1007/s11128-021-03021-3

Cited by 30 publications

(7 citation statements)

References 172 publications

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“…While D-Wave only applies quantum annealing (Section 4.2), IBM's gate-based universal quantum computer allows various algorithms. D-Wave has been selling quantum computers since 2011 (Nimbe et al, 2021) and has become an important player in the quantum computer industry (Gerbert and Ruess, 2018). Both D-Wave and IBM's methods involve time-evolving quantum systems (Crosson and Lidar, 2021), and the companies themselves have ambitious plans to shape the future of quantum computing4.…”

Section: Hardware and Benchmarkingmentioning

confidence: 99%

NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems

Rhonda

Chancellor²,

Halffmann³

2023

Front. Quantum Sci. Technol.

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In the last decade, public and industrial research funding has moved quantum computing from the early promises of Shor’s algorithm through experiments to the era of noisy intermediate scale quantum devices (NISQ) for solving real-world problems. It is likely that quantum methods can efficiently solve certain (NP-) hard optimization problems where classical approaches fail. In our perspective, we examine the field of quantum optimization, that is, solving optimization problems using quantum computers. We provide an entry point to quantum optimization for researchers from each topic, optimization or quantum computing, by demonstrating advances and obstacles with a suitable use case. We give an overview on problem formulation, available algorithms, and benchmarking. Although we show a proof-of-concept rather than a full benchmark between classical and quantum methods, this gives an idea of the current quality and capabilities of quantum computers for optimization problems. All observations are incorporated in a discussion on some recent quantum optimization breakthroughs, current status, and future directions.

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Section: Hardware and Benchmarkingmentioning

confidence: 99%

NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems

Rhonda

Chancellor²,

Halffmann³

2023

Front. Quantum Sci. Technol.

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“…There are several models of quantum computation (see [7] for an in-depth review) tailored in some cases to specific hardware technologies. But all of them share a common thread in that they allow the user to develop quantum software that exploits the unique effects of quantum mechanics, superposition and entanglement, to develop quantum algorithms that can achieve exponential speedups in solving some problems compared to the performance of their classical counter-parts (i.e., those running in digital computers).…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Approach for Modelling Quantum Circuits

Alonso,

Sánchez,

Álvarez

2023

Applied Sciences

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A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.

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“…It uses a different paradigm from classical information processing [2,3]. It also applies the phenomena and principles of quantum mechanics including superposition, entanglement, and interference which provide a better way to process information than the laws of classical physics [2,3,4,5,6,7,8]. QIP provides numerous advantages over classical information processing for certain problems [2,3,9] as indicated by the comparisons made in previous research [2,10].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, numerous big companies such as IBM, Intel, Microsoft, and Google have already been involved in the area of quantum computer development. Moreover, the exploration of new areas in quantum computing has led provided certain advantages [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

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Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

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Models in quantum computing: a systematic review

Cited by 30 publications

References 172 publications

NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems

NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems

A Graph-Based Approach for Modelling Quantum Circuits

Implementation of Quantum Annealing: A Systematic Review

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