To quantum or not to quantum: towards algorithm selection in near-term quantum optimization

Moussa, Charles; Calandra, Henri; Dunjko, Vedran

doi:10.48550/arxiv.2001.08271

Cited by 3 publications

(3 citation statements)

References 27 publications

(50 reference statements)

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“…We compare QAOA against a list of some of the best performing classical exact [18], approximate [19], and heuristic solvers [20] in terms of time-to-solution [21,22] and quality for a range of graph types [17,20,23,24]. One of the main contributions of this work is the definition of the framework for such comparison.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of QAOA based on the approximation ratio of individual samples

Larkin¹,

Jonsson²,

Justice³

et al. 2022

Quantum Sci. Technol.

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Abstract—The Quantum Approximate Optimization Al-gorithm (QAOA) is a hybrid quantum-classical algorithmto solve binary-variable optimization problems. Due to theshort circuit depth and its expected robustness to systematicerrors it is a promising candidates likely to run on near-term quantum devices. We simulate the performance ofQAOA applied to the Max-Cut problem and compareit with some of the best classical alternatives. Whencomparing solvers, their performance is characterized bythe computational time taken to achieve a given qualityof solution. Since QAOA is based on sampling, we utilizeperformance metrics based on the probability of observinga sample above a certain quality. In addition, we show thatthe QAOA performance varies significantly with the graphtype. In particular for 3-regular random graphs, QAOAperformance shows improvement by up to 2 orders of mag-nitude compared to previous estimates, strongly reducingthe performance gap with classical alternatives. This waspossible by reducing the number of function evaluationsper iteration and optimizing the variational parameterson small graph instances and transferring to large viatraining.Because QAOA’s performance guarantees areonly known for limited applications and contexts, we utilizea framework for the search for quantum advantage whichincorporates a large number of problem instances and allthree classical solver modalities: exact, approximate, andheuristic.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of QAOA based on the approximation ratio of individual samples

Larkin¹,

Jonsson²,

Justice³

et al. 2022

Quantum Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…We compare QAOA against a list of the best performing classical solvers [14], [15] in terms of time-tosolution [16], [17] and quality for a range of graph types [18]- [21]. One of the main contributions of this work is the definition of the framework for such comparison.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of QAOA based on the approximation ratio of individual samples

Larkin,

Jonsson,

Justice

et al. 2020

Preprint

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to its expected robustness to systematic errors and the short circuit depth, it is one of the promising candidates likely to run on near-term quantum devices. We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exact or approximate solution. When comparing approximate solvers, their performance is characterized by the computational time taken to achieve a given quality of solution. Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality. In addition, we show that the QAOA performance varies significantly with the graph type. By selecting a suitable optimizer for the variational parameters and reducing the number of function evaluations, QAOA performance improves by up to 2 orders of magnitude compared to previous estimates. Especially for 3-regular random graphs, this setting decreases the performance gap with classical alternatives.

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“…To address the gap around the existence of problems where a quantum advantage may be possible, recent work has involved the development of a framework for searching for QAOA-based advantages [249] and a classical machine learning approach for identifying problems that may offer an advantage [250]. These general approaches, designed to aid in the targeting of problems that may yield a quantum advantage, remain relatively unexplored.…”

Section: Quantum Approximate Optimization Algorithmsmentioning

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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To quantum or not to quantum: towards algorithm selection in near-term quantum optimization

Cited by 3 publications

References 27 publications

Evaluation of QAOA based on the approximation ratio of individual samples

Evaluation of QAOA based on the approximation ratio of individual samples

Evaluation of QAOA based on the approximation ratio of individual samples

Biology and medicine in the landscape of quantum advantages

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