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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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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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An Efficient QAOA via a Polynomial QPU-Needless Approach

An Efficient QAOA via a Polynomial QPU-Needless Approach

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Chicano, Francisco Abdelmoiz Dahi, Zakaria Luque, Gabriel Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9798400701207

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem's expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentiallyincreasing memory usage when dealing with large problems requiring more qubits. These limitations make today's QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some k-bounded pseudo-Boolean problems, optimizing the exact problem's expectation value can be done in polynomial time using a classical computer.

关键词： quantum approximate optimization algorithm Combinatorial optimization quantum Computing

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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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Exploiting In-Constraint Energy in Constrained Variational quantum optimization 3

Exploiting In-Constraint Energy in Constrained Variational Q...

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IEEE/ACM 3rd International Workshop on quantum Computing Software (QCS)

作者： Hao, Tianyi Shaydulin, Ruslan Pistoia, Marco Larson, Jeffrey Argonne Natl Lab Lemont IL 60439 USA Univ Wisconsin Madison Madison WI 53706 USA JPMorgan Chase New York NY 10017 USA

ISBN: (纸本)9781665475365

A central challenge of applying near-term quantum optimization algorithms to industrially relevant problems is the need to incorporate complex constraints. In general, such constraints cannot be easily encoded in the circuit, and the quantum circuit measurement outcomes are not guaranteed to respect the constraints. Therefore, the optimization must trade off the in-constraint probability and the quality of the in-constraint solution by adding a penalty for constraint violation into the objective. We propose a new approach for solving constrained optimization problems with unconstrained, easy-to-implement quantum ansatze. Our method leverages the in-constraint energy as the objective and adds a lower-bound constraint on the in-constraint probability to the optimizer. We demonstrate significant gains in solution quality over directly optimizing the penalized energy. We implement our method in QVoice, a Python package that interfaces with Qiskit for quick prototyping in simulators and on quantum hardware.

关键词： quantum approximate optimization algorithm variational quantum eigensolver constrained combinatorial optimization

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A quantum Approach to the Problem of Charging Electric Cars on a Motorway

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ENERGIES 2023年第1期16卷 442页

作者： Rozycki, Rafal Jozefowska, Joanna Kurowski, Krzysztof Lemanski, Tomasz Pecyna, Tomasz Subocz, Marek Waligora, Grzegorz Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Poznan Supercomp & Networking Ctr Inst Bioorgan Chem PL-61139 Poznan Poland

In this paper, the problem of charging electric motor vehicles on a motorway is considered. Charging points are located alongside the motorway. It is assumed that there are a number of vehicles on a given section of a motorway. In the motorway, there are several nodes, and for each vehicle, the entering and the leaving nodes are known, as well as the time of entrance. For each vehicle, we know the total capacity of its battery, and the current amount of energy in the battery when entering the motorway. It is also assumed that for each vehicle, there is a finite set of speeds it can use when traveling the motorway. The speed is chosen when entering the motorway, and cannot be changed before reaching the charging station. For each speed, there is given a corresponding power usage;the higher the speed, the larger the power usage. Each vehicle can only use one charger, and when its battery is full, the amount of energy is sufficient for reaching the outgoing node. We look for a feasible solution to the problem, i.e., a solution in which no vehicle has to wait for a charger. The problem is formulated as a problem of scheduling independent, nonpreemptable jobs in parallel, unrelated machines under an additional doubly constrained resource, which is power. quantum approaches to solve the defined problem are proposed. They use the quantum approximate optimization algorithm and the quantum annealing technique. A computational experiment is presented and discussed. Some conclusions and directions for future research are given.

关键词： electric motor vehicle battery charging power energy scheduling parallel unrelated machines quantum computing quantum approximate optimization algorithm quantum annealing

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Ising Hamiltonians for Constrained Combinatorial optimization Problems and the Metropolis-Hastings Warm-Starting algorithm

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ADVANCED quantum TECHNOLOGIES 2023年第9期6卷

作者： Li, Hui-Min Liang, Jin-Min Wang, Zhi-Xi Fei, Shao-Ming Capital Normal Univ Sch Math Sci Beijing 100048 Peoples R China

quantum approximate optimization algorithm (QAOA) is a promising variational quantum algorithm for combinatorial optimization problems. However, the implementation of QAOA is limited due to the requirement that the problems be mapped to Ising Hamiltonians and the nonconvex optimization landscapes. Although the Ising Hamiltonians for many NP hard problems have been obtained, a general method to obtain the Ising Hamiltonians for constrained combinatorial optimization problems (CCOPs) has not yet been investigated. In this paper, a general method is introduced to obtain the Ising Hamiltonians for CCOPs and the Metropolis-Hastings warm-starting algorithm for QAOA is presented which can provably converge to the global optimal solutions. The effectiveness of this method is demonstrated by tackling the minimum weight vertex cover (MWVC) problem, the minimum vertex cover (MVC) problem, and the maximal independent set problem as examples. The Ising Hamiltonian for the MWVC problem is obtained first time by using this method. The advantages of the Metropolis-Hastings warm-starting algorithm presented here is numerically analyzed through solving 30 randomly generated MVC cases with 1-depth QAOA.

关键词： constrained combinatorial optimization Metropolis-Hastings minimum weight vertex cover quantum approximate optimization algorithm

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Secure and Reliable Big-Data-Based Decision Making Using quantum Approach in IIoT Systems

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SENSORS 2023年第10期23卷 4852-4852页

作者： EL Azzaoui, Abir Salim, Mikail Mohammed Park, Jong Hyuk Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul 01811 South Korea

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs;instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

关键词： smart factory IIoT quantum algorithms quantum neural network quantum approximate optimization algorithm

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Opportunities and Challenges of quantum Computing for Engineering optimization

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第6期23卷 060817-060817页

作者： Wang, Yan Kim, Jungin E. Suresh, Krishnan Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA Univ Wisconsin Dept Mech Engn Madison WI 53706 USA

quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be utilized in solving optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.

关键词： quantum algorithm Grover search quantum phase estimation quantum annealing quantum approximate optimization algorithm variational quantum eigensolver quantum walk computational foundations for engineering optimization topology and shape optimization

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