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Review of Applications of quantum Computing in Power Flow Calculation

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第2期19卷 877-886页

作者： Jang, Ye-Eun Kim, Na-Yeon Kim, Young-Jin Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea

The proliferation of distributed energy resources has increased the complexity of power system analysis and operation. To address the complexity, various algorithms have been studied on classical computers, but their performance was constrained by hardware limitations of classical computers. As a new computing paradigm, quantum computing has recently been applied to power system operations to enhance computational efficiency, and early studies on quantum computing application have demonstrated the computational efficiency. Although there remains the limited scalability in current quantum devices, various algorithms have been developed by combining classical and quantum computers to exploit quantum computing fully. Therefore, with the brief introduction of quantum computing and its computing systems, this paper reviews and discusses the recent studies particularly on power flow and optimal power flow calculations using various quantum algorithms ranging from pure quantum computing algorithms to hybrid quantum-classical algorithms. In addition, this paper suggests new research subjects in power flow calculations for future studies.

关键词： quantum computing Power flow calculation Optimal power flow quantum circuit model quantum annealing model hybrid quantum-classical algorithm

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Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy quantum Computers

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON quantum ENGINEERING 2022年第1期3卷 1页

作者： Liu, Xiaoyuan Angone, Anthony Shaydulin, Ruslan Safro, Ilya Alexeev, Yuri Cincio, Lukasz Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Clemson Univ Sch Comp Clemson SC 29634 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Argonne Natl Lab Computat Sci Div Lemont IL 60439 USA Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA

Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to 40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results show that L-VQE performs well under realistic hardware noise.

关键词： Combinatorial optimization hybrid quantum-classical algorithm quantum optimization

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hybrid quantum-classical convolutional neural networks

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Science China(Physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

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quantum-compliant users scheduling optimization in joint transmission mobile access networks

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quantum INFORMATION PROCESSING 2024年第7期23卷 262-262页

作者： Volpe, Deborah Cirillo, Giovanni Amedeo Fantini, Roberto Boella, Andrea Mondo, Giovanni Graziano, Mariagrazia Turvani, Giovanna Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 Turin Italy TIM SpA Res & Dev Via Reiss Romoli Guglielmo I-10148 Turin Italy Politecn Torino Dept Appl Sci & Technol Corso Duca Abruzzi 24 I-10129 Turin Italy

Joint Transmission (JT) is the dynamic coordination of transmission and/or reception at multiple geographically separated sites to improve end-user service quality. When user equipment receives signals from multiple sites, downstream performance improves. An optimization problem arises in selecting the best user subset for JT within a multiple-input-multiple-output (MIMO) system. Unfortunately, a pure brute-force approach is not feasible due to exponential time growth with user combinations, unsuitable for real-time selection in mobile networks with users continuously changing in time. This article proposes quantum-compliant heuristics using quadratic unconstrained binary optimization (QUBO) for JT user scheduling. QUBO handles initial user selection, followed by brute-force exploration for the solution. Numerical results indicate that quantum-compliant methods decrease solution time without substantial accuracy loss compared to brute-force methods.

关键词： Joint transmission Multiuser multiple-input-multiple-output Multi-users scheduling optimization Quadratic unconstrained binary optimization quantum computing hybrid quantum-classical algorithm

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Toward a quantum computing algorithm to quantify classical and quantum correlation of system states

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quantum INFORMATION PROCESSING 2021年第12期20卷 1-17页

作者： Mahdian, M. Yeganeh, H. Davoodi Univ Tabriz Fac Phys Theoret & Astrophys Dept Tabriz 51665163 Iran

Optimal measurement is required to obtain the quantum and classical correlations of a quantum state, and the crucial difficulty is how to acquire the maximal information about one system by measuring the other part;in other words, getting the maximum information corresponds to preparing the best measurement operators. Within a general setup, we designed a variational hybrid quantum-classical algorithm to achieve classical and quantum correlations for system states under the Noisy-Intermediate Scale quantum technology. To employ, first, we map the density matrix to the vector representation, which displays it in a doubled Hilbert space, and it is converted to a pure state. Then, we apply the measurement operators to a part of the subsystem and use variational principle and a classical optimization for the determination of the amount of correlation. We numerically test the performance of our algorithm at finding a correlation of some density matrices, and the output of our algorithm is compatible with the exact calculation.

关键词： hybrid quantum-classical algorithm Optimal measurement quantum correlation quantum discord

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QDNN: deep neural networks with quantum layers

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quantum MACHINE INTELLIGENCE 2021年第1期3卷 1-9页

作者： Zhao, Chen Gao, Xiao-Shan Acad Math & Syst Sci Chinese Acad Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In this paper, a quantum extension of classical deep neural network (DNN) is introduced, which is called QDNN and consists of quantum structured layers. It is proved that the QDNN can uniformly approximate any continuous function and has more representation power than the classical DNN. Moreover, the QDNN still keeps the advantages of the classical DNN such as the non-linear activation, the multi-layer structure, and the efficient backpropagation training algorithm. Furthermore, the QDNN uses parameterized quantum circuits (PQCs) as the basic building blocks and hence can be used on near-term noisy intermediate-scale quantum (NISQ) processors. A numerical experiment for an image classification task based on QDNN is given, where a high accuracy rate is achieved.

关键词： Deep neural networks quantum machine learning hybrid quantum-classical algorithm NISQ

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A hybrid quantum-classical neural network for learning transferable visual representation

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quantum SCIENCE AND TECHNOLOGY 2023年第4期8卷 045021-045021页

作者： Wang, Ruhan Richerme, Philip Chen, Fan Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN 47405 USA Indiana Univ Dept Phys Bloomington IN USA

State-of-the-art quantum machine learning (QML) algorithms fail to offer practical advantages over their notoriously powerful classical counterparts, due to the limited learning capabilities of QML algorithms, the constrained computational resources available on today's noisy intermediate-scale quantum (NISQ) devices, and the empirically designed circuit ansatz for QML models. In this work, we address these challenges by proposing a hybrid quantum-classical neural network (CaNN), which we call QCLIP, for quantum Contrastive Language-Image Pre-Training. Rather than training a supervised QML model to predict human annotations, QCLIP focuses on more practical transferable visual representation learning, where the developed model can be generalized to work on unseen downstream datasets. QCLIP is implemented by using CaNNs to generate low-dimensional data feature embeddings followed by quantum neural networks to adapt and generalize the learned representation in the quantum Hilbert space. Experimental results show that the hybrid QCLIP model can be efficiently trained for representation learning. We evaluate the representation transfer capability of QCLIP against the classical Contrastive Language-Image Pre-Training model on various datasets. Simulation results and real-device results on NISQ IBM_Auckland quantum computer both show that the proposed QCLIP model outperforms the classical CLIP model in all test cases. As the field of QML on NISQ devices is continually evolving, we anticipate that this work will serve as a valuable foundation for future research and advancements in this promising area.

关键词： hybrid quantum-classical algorithm quantum neural network variational quantum circuit NISQ contrastive language-image pre-training zero-shot learning

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quantum adversarial metric learning model based on triplet loss function

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EPJ quantum TECHNOLOGY 2023年第1期10卷 24页

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang ZaoZhuang Univ Coll Informat Sci & Engn Zaozhuang 277160 Shandong Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Beijing Univ Posts Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher & epsilon;-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning.

关键词： Metric learning hybrid quantum-classical algorithm quantum machine learning

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hybrid Phase-Based Representation of quantum Images

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INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS 2023年第6期62卷 115-115页

作者： Mandal, Arijit Banerjee, Shreya Panigrahi, Prasanta K. Indian Inst Technol Madras Dept Phys Chennai 600036 Tamil Nadu India Univ Sherbrooke Dept Phys Sherbrooke PQ J1K 2R1 Canada Univ Sherbrooke Inst Quant Sherbrooke PQ J1K 2R1 Canada Indian Inst Sci Educ & Res Kolkata Dept Phys Sci Mohanpur 741246 W Bengal India

We propose a novel and hybrid quantum-classical algorithm that requires only O(log HW) qubits and reduces the multi-qubit gate costs required to represent an image of dimension (H x W). In this algorithm, no qubit is needed to store the color information of the image. We represent the location information of an image with a superposition of mutually orthogonal vectors of an arbitrary basis and store the pixel information in the phases of the corresponding basis vectors without any extra qubit cost. We further present a classical algorithm to encode the phases and show that the inclusion of the classical algorithm significantly reduces the number of multi-qubit quantum gates required for image representation. Finally, we implement our algorithm on the classical simulator provided by IBM quantum as a proof of concept.

关键词： quantum images hybrid quantum-classical algorithm quantum circuit Phase updation algorithm

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Performance analysis of a hybrid agent for quantum-accessible reinforcement learning

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NEW JOURNAL OF PHYSICS 2022年第3期24卷 033044页

作者： Hamann, Arne Woelk, Sabine Univ Innsbruck Inst Theoret Phys Technikerstr 21a A-6020 Innsbruck Austria German Aerosp Ctr DLR Inst Quantum Technol D-89081 Ulm Germany

In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning (RL). In RL, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average < J > rewards and < T >(cl) epochs to learn how to solve the task, the hybrid agent will take < T >(q) <= alpha(s)alpha(o)root < T >(cl)< J > epochs on average. Here, alpha(s) and alpha(o) denote constants depending on details of the quantum search and are independent of the problem size. Additionally, we prove that if the environment allows for maximally alpha(o)k(max). sequential coherent interactions, e.g. due to noise effects, an improvement given by < T >(q) approximate to alpha(o) < T >(cl/)(4k(max)) is still possible.

关键词： quantum reinforcement learning reinforcement learning amplitude amplification hybrid quantum-classical algorithm quantum search

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