Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way

Martiel, Simon; Ayral, Thomas; Allouche, Cyril

doi:10.1109/tqe.2021.3090207

Cited by 21 publications

(6 citation statements)

References 29 publications

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“…Another recent interesting metric is the "Q-score" [68] conceived by the technology consulting company Atos. The Q-score measures the maximum number of qubits effectively employed to solve combinatorial optimization problem.…”

Section: Benchmarking Metrics For Quantum Optimizationmentioning

confidence: 99%

Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing

ARMAS,

DELEPLANQUE,

CREEMERS

2024

Preprint

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Quantum annealing emerges as a viable solution for solving complex problems such as the resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer Linear Programming formulations in an attempt to solve the RCPSP, and convert the most qubit-efficient formulation into a Quadratic Unconstrained Binary Optimization (QUBO) model. We solve this QUBO model using the D-Wave Advantage 6.3 Quantum Annealing (QA) machine and compare its performance with that of classical computer solvers. This pioneering effort marks the first use of quantum annealing for RCPSP, and shows promising results, especially for small to medium-sized instances.

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Section: Benchmarking Metrics For Quantum Optimizationmentioning

confidence: 99%

Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing

ARMAS,

DELEPLANQUE,

CREEMERS

2024

Preprint

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“…Another way to quantify the performance of QC are the application-oriented benchmarks, in which a specific problem is identified, solved and checked how quickly or well that happens. An example of this is the Q-Score developed by ATOS [29]. This score is determined by the approximate solutions of the Max-Cut problem for specific graphs, (N, 1 2 ) Erdös-Rényi graphs, for increasingly larger graphs.…”

Section: Computational Operations Researchmentioning

confidence: 99%

Searching for Optimisation

Phillipson¹

2022

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“…In February 2021, Atos Quantum Laboratories proposed a new benchmark, dubbed the Atos Q-score [18]. The Q-score is defined as the maximum number of qubits that can be used effectively to solve the MaxCut combinatorial optimization problem using QAOA.…”

Section: Quantum Application-oriented Benchmarkingmentioning

confidence: 99%

“…This is the number of qubits that a quantum computer is able to run within a margin of the desired output accuracy. This is a similar approach as the Atos Q-score [18], but generalized over multiple quantum applications and using the QuEST simulator with the classical optimizer as a baseline. The capacity score is then the highest number of qubits Q N corresponding to problem size N for which the quantum computer can achieve a relative error within a set threshold accuracy A * .…”

Section: Capacitymentioning

confidence: 99%

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QPack Scores: Quantitative performance metrics for application-oriented quantum computer benchmarking

Donkers¹,

Mesman²,

Al-Ars³

et al. 2022

Preprint

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This paper presents the benchmark score definitions of QPack, an application-oriented cross-platform benchmarking suite for quantum computers and simulators, which makes use of scalable Quantum Approximate Optimization Algorithm and Variational Quantum Eigensolver applications. Using a varied set of benchmark applications, an insight of how well a quantum computer or its simulator performs on a general NISQ-era application can be quantitatively made. This paper presents what quantum execution data can be collected and transformed into benchmark scores for application-oriented quantum benchmarking. Definitions are given for an overall benchmark score, as well as sub-scores based on runtime, accuracy, scalability and capacity performance. Using these scores, a comparison is made between various quantum computer simulators, running both locally and on vendors' remote cloud services. We also use the QPack benchmark to collect a small set of quantum execution data of the IBMQ Nairobi quantum processor. The goal of the QPack benchmark scores is to give a holistic insight into quantum performance and the ability to make easy and quick comparisons between different quantum computers.

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Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way

Cited by 21 publications

References 29 publications

Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing

Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing

Searching for Optimisation

QPack Scores: Quantitative performance metrics for application-oriented quantum computer benchmarking

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