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检索条件"主题词=Quantum Approximate Optimization Algorithm"

共 69 条记录，以下是41-50 订阅

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Characterizing Error Mitigation by Symmetry Verification in QAOA 3

Characterizing Error Mitigation by Symmetry Verification in ...

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3rd IEEE International Conference on quantum Computing and Engineering (QCE)

作者： Kakkar, Ashish Larson, Jeffrey Galda, Alexey Shaydulin, Ruslan Univ Kentucky Dept Phys & Astron Lexington KY 40506 USA Argonne Natl Lab Math & Comp Sci Lemont IL USA Menten AI Inc San Francisco CA USA JPMorgan Chase Global Technol Appl Res New York NY USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state fidelity, the expected solution quality, and the success probability of QAOA on a superconducting quantum processor. Symmetry verification uses parity checks that leverage the symmetries of the objective function to be optimized. We develop a theoretical framework for analyzing this approach under local noise and derive explicit formulas for fidelity improvements on problems with global Z(2) symmetry. We numerically investigate the symmetry verification on the MaxCut problem and identify the error regimes in which this approach improves the QAOA objective. We observe that these regimes correspond to the error rates present in near-term hardware. We further demonstrate the efficacy of symmetry verification on an IonQ trapped ion quantum processor where an improvement in the QAOA objective of up to 19.2% is observed.

关键词： quantum computing error mitigation quantum optimization quantum approximate optimization algorithm QAOA trapped-ion quantum processor

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A quantum Minimum Cut-Set Method for Vulnerable Node Localization Against False Data Injection Attacks

A Quantum Minimum Cut-Set Method for Vulnerable Node Localiz...

引用

Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Wu, Zhuoheng Liu, Yuan Liang, Hao

ISBN: (纸本)9798350323979

Along with the population of the smart grid, the potential vulnerabilities of the power system in the cyber-security aspect are increasing. False data injection attacks (FDIA), which can stealthily bypass the system operator's bad data detection (BDD), threaten the power system the most. FDIA is typically designed to attack the vulnerable nodes of the system. The system operator needs to locate those vulnerable nodes and further defend against the FDIA. In this paper, we consider using quantum computing to locate those vulnerable nodes. A quantum vulnerable node location framework (QVNLF) is designed to locate vulnerable nodes against FDIA by applying the quantum approximate optimization algorithm (QAOA). We also prove that attacking those vulnerable nodes located by the minimum cut-set (MCS) method is the most harmful to the system. The performance of our proposed method is evaluated in the case study based on IEEE 5-Bus Transmission System and IEEE 9-Bus Transmission System, respectively. The results obtained from our method are the same as the results obtained from the traditional Stoer-Wagner algorithm, which verifies the effectiveness of our proposed framework based on quantum computing.

关键词： False data injection attacks minimum cut-set quantum approximate optimization algorithm quantum computing smart grid

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quantum Potential Speed-Up of Combinatorial optimization Solvers for AUV Path Planning

Quantum Potential Speed-Up of Combinatorial Optimization Sol...

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OCEANS Conference

作者： Monbroussou, Leo Kukla, Romain Ecole Normale Super Paris Saclay Gif Sur Yvette France Naval Grp Res Naval Grp Ollioules France

ISBN: (纸本)9780692935590

quantum technology enters in the new era of Noisy Intermediate-Scale quantum (NISQ) with 50-100 qubits quantum computers already available. It is now important to compare quantum methods with the classical approach to solve optimization problems in order to determine if there is a potential speed-up and what quantum technologies will be able to perform. The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems. We provide the method to apply QAOA on a shortest path problem and a study of the performance of this quantum method by using heuristic strategies based on observed patterns in optimal parameters. First, we will present the state of the art of quantum hybrid algorithms and technologies. Then we will discuss the effect of the shape of the graph, the number of valid solutions, and the number of nodes of the graph we consider on the quality of the QAOA's results and on the patterns in optimal variational parameters.

关键词： Vehicule navigation Autonomous underwater vehicles quantum approximate optimization algorithm Combinatorial problem

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A hybrid quantum-classical approach to warm-starting optimization 4

A hybrid quantum-classical approach to warm-starting optimiz...

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Conference on quantum Computing, Communication, and Simulation IV

作者： Dehn, Vanessa Wellens, Thomas Fraunhofer Inst Appl Solid State Phys IAF Tullastr 72 D-79108 Freiburg Germany

ISBN: (纸本)9781510670839;9781510670822

The quantum approximate optimization algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting the standard algorithm improves the performance. In this paper we compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization and investigate the warm-start approach for different problem instances. In particular, we analyze the extent to which the improved performance of warm-start QAOA is due to quantum effects, and show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.

关键词： quantum optimization quantum approximate optimization algorithm

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Fuzzy Clustering for QAOA Complexity Reduction

Fuzzy Clustering for QAOA Complexity Reduction

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Acampora, Giovanni Chiatto, Angela Vitiello, Autilia Univ Naples Federico II Dept Phys Ettore Pancini Naples Italy

ISBN: (纸本)9798350332285

The quantum approximate optimization algorithm (QAOA) has become one of the most widely used components in the development of modern quantum applications. It works on the paradigm of quantum variational circuits, where a quantum circuit is trained - by repeatedly adjusting circuit parameters - to adequately solve a combinatorial optimization problem. This training process, based on classical optimization algorithms, represents a significant computational bottleneck for QAOA, as it requires repeated calls to the quantum device to evaluate the cost function of the problem to solve. Therefore, there is a strong need to eliminate this computationally expensive task and identify an alternative strategy to compute good parameters for QAOA. This paper synergistically exploits the parameter concentration property of QAOA and the Fuzzy C-Means algorithm to achieve this goal. Experimental results show that the proposed approach can support QAOA to maintain high performance in solving well-known optimization problems such as MAXCUT, requiring a reduced computational effort for the parameter tuning phase.

关键词： Fuzzy C-Means quantum approximate optimization algorithm quantum Computing

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Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA

Evidence that PUBO outperforms QUBO when solving continuous ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Stein, Jonas Chamanian, Farbod Zorn, Maximilian Nuesslein, Jonas Zielinski, Sebastian Koelle, Michael Linnhoff-Popien, Claudia Ludwig Maximilians Univ Munchen Munich Bavaria Germany

ISBN: (纸本)9798400701207

quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimization problems with known quantum algorithms such as the quantum approximate optimization algorithm (QAOA) is the problem formulation. While quantum optimization has historically centered around Quadratic Unconstrained optimization (QUBO) problems, recent studies show, that many combinatorial problems such as the TSP can be solved more efficiently in their native Polynomial Unconstrained optimization (PUBO) forms. As many optimization problems in practice also contain continuous variables, our contribution investigates the performance of the QAOA in solving continuous optimization problems when using PUBO and QUBO formulations. Our extensive evaluation on suitable benchmark functions, shows that PUBO formulations generally yield better results, while requiring less qubits. As the multi-qubit interactions needed for the PUBO variant have to be decomposed using the hardware gates available, i.e., currently single- and two-qubit gates, the circuit depth of the PUBO approach outscales its QUBO alternative roughly linearly in the order of the objective function. However, incorporating the planned addition of native multi-qubit gates such as the global Molmer-Sorenson gate, our experiments indicate that PUBO outperforms QUBO for higher order continuous optimization problems in general.

关键词： quantum Computing Continuous optimization quantum approximate optimization algorithm Quadratic Unconstrained Binary optimization Polynomial Unconstrained Binary optimization

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Characterizing the Effects of Zero-Noise Extrapolation on a QAOA Workflow 5

Characterizing the Effects of Zero-Noise Extrapolation on a ...

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2024 International Conference on quantum Computing and Engineering

作者： Venere, Marco Lusso, Adriano Onofre, Victor Maldonado-Romo, Alberto Santambrogio, Marco D. Politecn Milan Milan Italy Univ Nacl Comahue Buenos Aires DF Argentina Quantum Open Source Fdn Toronto ON Canada

ISBN: (纸本)9798331541378

The effects of noise on NISQ devices strongly limit the current applicability of quantum combinatorial optimization algorithms. In this work, we integrate ZNE, an error mitigation technique, into the QAOA, thus adding further reliability to the method. We also perform a benchmark to assess the effects of mitigation on the exploration of the parameters space performed by the optimizer.

关键词： quantum approximate optimization algorithm quantum error mitigation zero-noise extrapolation

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A Predictive Approach for Selecting the Best quantum Solver for an optimization Problem 5

A Predictive Approach for Selecting the Best Quantum Solver ...

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2024 International Conference on quantum Computing and Engineering

作者： Volpe, Deborah Quetschlich, Nils Graziano, Mariagrazia Turvani, Giovanna Wille, Robert Politecn Torino Dept Elect & Telecommun Turin Italy Tech Univ Munich Chair Design Automat Munich Germany Politecn Torino Dept Appl Sci & Technol Turin Italy Software Competence Ctr Hagenberg GmbH SCCH Hagenberg Austria

ISBN: (纸本)9798331541378

Leveraging quantum computers for optimization problems holds promise across various application domains. Nevertheless, utilizing respective quantum computing solvers requires describing the optimization problem according to the Quadratic Unconstrained Binary optimization (QUBO) formalism and selecting a proper solver for the application of interest with a reasonable setting. Both demand significant proficiency in quantum computing, QUBO formulation, and quantum solvers, a background that usually cannot be assumed by end users who are domain experts rather than quantum computing specialists. While tools aid in QUBO formulations, support for selecting the best-solving approach remains absent. This becomes even more challenging because selecting the best solver for a problem heavily depends on the problem itself. In this work, we are accepting this challenge and propose a predictive selection approach, which aids end users in this task. To this end, the solver selection task is first formulated as a classification task that is suitable to be solved by supervised machine learning. Based on that, we then propose strategies for adjusting solver parameters based on problem size and characteristics. Experimental evaluations, considering more than 500 different QUBO problems, confirm the benefits of the proposed solution. In fact, we show that in more than 70% of the cases, the best solver is selected, and in about 90% of the problems, a solver in the top two, i.e., the best or its closest suboptimum, is selected. This exploration proves the potential of machine learning in quantum solver selection and lays the foundations for its automation, broadening access to quantum optimization for a wider range of users. The pre-trained classifier is integrated into the MQT quantum Auto Optimizer (MQT QAO) framework, publicly available on GitHub (https://***/cda-tum/mqt-qao) as part of the Munich quantum Toolkit (MQT).

关键词： Machine Learning Predictive Models QUBO quantum optimization quantum Annealer quantum approximate optimization algorithm Variational quantum Eigensolver Grover Adaptive Search

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HamilToniQ: An Open -Source Benchmark Toolkit for quantum Computers 5

HamilToniQ: An Open -Source Benchmark Toolkit for Quantum Co...

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2024 International Conference on quantum Computing and Engineering

作者： Xu, Xiaotian Chen, Kuan-Cheng Wille, Robert Imperial Coll London QuEST London England Tech Univ Munich Chair Design Automat Munich Germany

ISBN: (纸本)9798331541378

This paper introduces HamilToniQ, an open -source benchmarking toolkit for quantum Processing Units (QPUs). It addresses the complexities of quantum computations by providing a methodological framework to assess QPU types, topologies, and systems. HamilToniQ facilitates performance evaluations through steps like circuit compilation and quantum error mitigation, with strategies tailored for each stage. The toolkit's H -Score measures QPU fidelity and reliability, offering a comprehensive view of performance. Focused on the quantum approximate optimization algorithm (QAOA), HamilToniQ enables consistent QPU comparisons, enhancing benchmarking transparency. Validated on various IBM QPUs, the toolkit proves effective and robust, advancing quantum computing with precise benchmarking metrics.

关键词： Benchmarking Program Characterization optimization quantum approximate optimization algorithm quantum Computing quantum Error Correction

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QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA 2

QAOAKit: A Toolkit for Reproducible Study, Application, and ...

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2nd International Workshop on quantum Computing Software (QCS)

作者： Shaydulin, Ruslan Marwaha, Kunal Wurtz, Jonathan Lotshaw, Phillip C. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Calif Berkeley Berkeley Ctr Quantum Informat & Computat Berkeley CA 94720 USA Tufts Univ Dept Phys & Astron Medford MA 02155 USA Oak Ridge Natl Lab Quantum Computat Sci Grp Oak Ridge TN 37830 USA

ISBN: (纸本)9781728186740

Understanding the best known parameters, performance, and systematic behavior of the quantum approximate optimization algorithm (QAOA) remain open research questions, even as the algorithm gains popularity. We introduce QAOAKit, a Python toolkit for the QAOA built for exploratory research. QAOAKit is a unified repository of preoptimized QAOA parameters and circuit generators for common quantum simulation frameworks. We combine, standardize, and cross-validate previously known parameters for the MaxCut problem, and incorporate this into QAOAKit. We also build conversion tools to use these parameters as inputs in several quantum simulation frameworks that can be used to reproduce, compare, and extend known results from various sources in the literature. We describe QAOAKit and provide examples of how it can be used to reproduce research results and tackle open problems in quantum optimization.

关键词： quantum approximate optimization algorithm open quantum software

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