Evaluating the Q-score of Quantum Annealers

Schoot, Ward van der; Leermakers, Daan; Wezeman, Robert; Neumann, Niels M. P.; Phillipson, Frank

doi:10.1109/qsw55613.2022.00017

Cited by 10 publications

(6 citation statements)

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“…Adapting the Q-score methodology for QA, as proposed by Van der Schoot et al [64], we computed the β(n) values for various instance sizes and types examined in our study. Analysis of Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Both datasets offer a large number of hard instances of varying sizes (where size is defined by the number of project activities, ranging from 20 to 120 activities). However, considering the limitations on the number of available qubits in the D-wave Advantage 6.3 Quantum Annealer, we decided to utilize the RanGen instance generator proposed by Demeulemeester, Vanhoucke, and Herroelen [64,65]. This generator allows the creation of random instances with varying levels of difficulty.…”

Section: Rcpsp Instance Selection Protocolmentioning

confidence: 99%

“…The Q-score measures the maximum number of qubits effectively employed to solve combinatorial optimization problem. Originally, the Q-score was developed to evaluate the solution of the Max-Cut problem using the Quantum Approximate Optimization Algorithm [69] (QAOA); however, this metric can be readily adapted to assess the performance of quantum annealing, demonstrated by Van der Schoot et al [64]. The Q-score of a given problem can be then calculated using:…”

Section: Benchmarking Metrics For Quantum Optimizationmentioning

confidence: 99%

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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: Numerical Resultsmentioning

confidence: 99%

Section: Rcpsp Instance Selection Protocolmentioning

confidence: 99%

Section: Benchmarking Metrics For Quantum Optimizationmentioning

confidence: 99%

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

ARMAS,

DELEPLANQUE,

CREEMERS

2024

Preprint

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“…Benchmark frameworks such as Super-marQ [33] and QPack Scores [34] include one or more QAOA applications in their sample benchmarks, while QUARK [71] considers specific optimization problems arising in industry. The Q-score metric [72] is claimed to be applicable to quantum processors in several categories, measuring the size of the largest graph for which the solver outperforms random guessing within a fixed time limit. All references present results that measure solution validity, feasibility, and run-time on several backend quantum computers, some on both gate model and quantum annealing devices.…”

Section: Previous Workmentioning

confidence: 99%

Optimization Applications as Quantum Performance Benchmarks

Lubinski¹,

Coffrin²,

McGeoch³

et al. 2023

Preprint

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Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing opensource QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.CONTENTS * This work was sponsored by the Quantum Economic Development Consortium (QED-C) and was performed under the auspices of the QED-C Technical Advisory Committee on Standards and Performance Benchmarks. The authors acknowledge many committee members for their input to and feedback on the project and this manuscript.

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“…Indeed, an argument has been made about the current field of quantum computer benchmarking, stressing the point that we are still in the exploratory stage [9]. The last few years have brought the arrival of the first quantum benchmarks, the most prominent ones being the Quantum Volume (QV) [10,11] and the Q-score [12]. Yet, the field is only starting and there is still a long road ahead.…”

Section: Introductionmentioning

confidence: 99%

Testing quantum computers with the protocol of quantum state matching

Ortega

Kálmán²,

Kiss³

2023

Phys. Scr.

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The presence of noise in quantum computers hinders their effective operation. Even though quantum error correction can theoretically remedy this problem, its practical realization is still a challenge. Testing and benchmarking noisy, intermediate-scale quantum (NISC) computers is therefore of high importance. Here, we suggest the application of the so-called quantum state matching protocol for testing purposes. This protocol was originally proposed to determine if an unknown quantum state falls in a prescribed neighborhood of a reference state. We decompose the unitary specific to the protocol and construct the quantum circuit implementing one step of the dynamics for different characteristic parameters of the scheme and present test results for two different IBM quantum computers. By comparing the experimentally obtained relative frequencies of success to the ideal success probability with a maximum statistical tolerance, we discriminate statistical errors from device specific ones. For the characterization of noise, we also use the fact that while the output of the ideal protocol is insensitive to the internal phase of the input state, the actual implementation may lead to deviations. For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume, while for random inputs they show a more similar performance.

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Evaluating the Q-score of Quantum Annealers

Cited by 10 publications

References 0 publications

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

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

Optimization Applications as Quantum Performance Benchmarks

Testing quantum computers with the protocol of quantum state matching

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