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IEEE TRANSACTIONS ON quantum ENGINEERING 2025年第1期6卷 1-12页

作者： Sato, Rei Gordon, Cui Saito, Kazuhiro Kawashima, Hideyuki Nikuni, Tetsuro Watabe, Shohei KDDI Res Inc Quantum Comp Grp Fujimino 3568502 Japan Tokyo Univ Sci Dept Phys Tokyo 1628601 Japan Keio Univ Fac Environm & Informat Studies Fujisawa 2520882 Japan Shibaura Inst Technol Fac Engn Comp Sci & Engn Tokyo 1358548 Japan

quantum search algorithms, such as Grover's algorithm, are anticipated to efficiently solve constrained combinatorial optimization problems. However, applying these algorithms to the traveling salesman problem (TSP) on a quantum circuit presents a significant challenge. Existing quantum search algorithms for the TSP typically assume that an initial state-an equal superposition of all feasible solutions satisfying the problem's constraints-is pre-prepared. The query complexity of preparing this state using brute-force methods scales exponentially with the factorial growth of feasible solutions, creating a significant hurdle in designing quantum circuits for large-scale TSPs. To address this issue, we propose a two-step quantum search (TSQS) algorithm that employs two sets of operators. In the first step, all the feasible solutions are amplified into their equal superposition state. In the second step, the optimal solution state is amplified from this superposition state. The TSQS algorithm demonstrates greater efficiency compared to conventional search algorithms that employ a single oracle operator for finding a solution within the encoded space. Encoded in the higher order unconstrained binary optimization representation, our approach significantly reduces the qubit requirements. This enables efficient initial state preparation through a unified circuit design, offering a quadratic speedup in solving the TSP without prior knowledge of feasible solutions.

关键词： Costs Urban areas Optimization Search problems Encoding quantum circuit Complexity theory Traveling salesman problems quantum algorithm Heuristic algorithms Grover's algorithm quantum search algorithms traveling salesman problems (TSPs)

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Portfolio optimization based on quantum linear algorithm

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PHYSICA SCRIPTA 2024年第8期99卷 085107-085107页

作者： Guo, Zhengming Song, Tingting Lin, Ge Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

The rapid development of quantum computation has brought new possibilities to many fields. Especially in finance, quantum computing offers significant advantages. Recently, the portfolio optimization problem has been solved by a quantum algorithm with a mean-variance model with sparse data. However, the mean-variance model does not match the practice, and furthermore, the data is mostly dense. To fill the gap, we propose the quantum-Enhanced Portfolio Optimization based on the mean-semi-variance model, where the mean-semi-variance model incorporates an optimized risk definition. The algorithm also effectively reduces the time complexity of solving high-dimensional linear systems and achieves sparsity independence.

关键词： quantum algorithm portfolio optimization mean-semi-variance model sparsity independent

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Study of Attacks on the HHL quantum algorithm

Study of Attacks on the HHL Quantum Algorithm

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Yizhuo Tan Hrvoje Kukina Jakub Szefer Yale University New Haven US TU Wien Vienna Austria

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

As the quantum research community continues to grow and new algorithms are designed, developed, and implemented, it is crucial to start thinking about security aspects and potential threats that could result in misuse of the algorithms, or jeopardize the information processed with these quantum algorithms. This work focuses on exploration of two types of potential attacks that could be deployed on a cloud-based quantum computer by an attacker circuit trying to interfere with victim circuit. The two attacks, called Improper Initialization Attack (IIA) and Higher Energy Attack (HEA), are for the first time applied to a well-known and widely used quantum algorithm: HHL. The HHL algorithm is used in the field of machine learning and big data for solving systems of linear equations. This work evaluates the effect of the attacks on different qubits within the HHL algorithm: ancilla qubit, clock qubit, and b qubit. This work demonstrates that the two attacks are able to cause incorrect results, even when only one of the qubits in the victim algorithm is attacked. Having discovered the vulnerabilities, the work motivates the need for future work to develop defense strategies for each of these attack scenarios.

关键词： Privacy quantum algorithm Machine learning algorithms Qubit Machine learning Big Data Intelligent systems Computer security Clocks

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quantum algorithm for the Simulation of Squamous Cell Lung Carcinoma tested through IBM quantum Composer

Quantum Algorithm for the Simulation of Squamous Cell Lung C...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Akshita Tiwari Academy of Science Academies of Loudoun Leesburg United States

Classical algorithms for lung cancer exist based on immunotherapy but are quite prone to error. Non-small cell is a major type of lung cancer, frequently caused by smoking. A classical algorithm to treat non-small-cell lung cancer is compared to a quantum algorithm created. Using superposition of qubits, Born’s rule, and the general qubit rule, an algorithm simulates the pathway of certain driver genes, by finding the angle required of the qubit to be able to result in the probability needed to simulate the pathway, tested in IBM quantum Composer.

关键词： quantum algorithm Computational modeling Qubit Lung cancer Lung Immunotherapy

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quantum Circuit Ansatz: Patterns of Abstraction and Reuse of quantum algorithm Design

Quantum Circuit Ansatz: Patterns of Abstraction and Reuse of...

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quantum Software (QSW), IEEE International Conference on

作者： Xiaoyu Guo Takahiro Muta Jianjun Zhao Kyushu University

ISBN: (数字)9798350368475

ISBN: (纸本)9798350368482

quantum computing holds the potential to revolutionize various fields by efficiently tackling complex problems. At its core are quantum circuits, sequences of quantum gates manipulating quantum states. The selection of the right quantum circuit ansatz, which defines initial circuit structures and serves as the basis for optimization techniques, is crucial in quantum algorithm design. This paper presents a categorized catalog of quantum circuit ansatzes aimed at supporting quantum algorithm design and implementation. Each ansatz is described with details such as intent, motivation, applicability, circuit diagram, implementation, example, and see also. Practical examples are provided to illustrate their application in quantum algorithm design. The catalog aims to assist quantum algorithm designers by offering insights into the strengths and limitations of different ansatzes, thereby facilitating decision-making for specific tasks.

关键词： Technological innovation quantum algorithm Machine learning algorithms Codes Software algorithms Collaboration Logic gates

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A Flexible Hybrid quantum algorithm for Vehicle Routing

A Flexible Hybrid Quantum Algorithm for Vehicle Routing

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quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Arun Vellat Sadashivan Robin Ajmera Shantom Kumar Borah Akansha Kumar Shailesh Kumar Jio Platforms Ltd Bengaluru India Dept. of Electrical and Computer Engineering University of Arizona Tucson USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The capacitated vehicle routing problem (CVRP) is an important problem for logistics optimization in supply chain. We propose a novel hybrid classical quantum algorithm that works by splitting the problem into a quantum travelling salesman problem (TSP) phase and a partitioning phase. In order to validate our methods, we run tests using D-Wave's Leap framework on random test cases, as well as a real-world data set from the retail domain. We run a comparison study against existing state of the art hybrid quantum-classical approaches for the CVRP, in addition to classical heuristic methods. Our method is shown to have the additional flexibility to handle fleet extension, and the solution quality is at least as good as the considered state of the art quantum hybrid algorithms. Further, we show that the algorithm can easily be modified for tackling other realistic vehicle routing variants.

关键词： quantum algorithm Annealing Vehicle routing Supply chains quantum annealing Optimization

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Variational quantum algorithm Landscape Reconstruction by Low-Rank Tensor Completion

Variational Quantum Algorithm Landscape Reconstruction by Lo...

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quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Tianyi Hao Zichang He Ruslan Shaydulin Marco Pistoia Swamit Tannu Department of Computer Sciences University of Wisconsin-Madison Madison WI USA Global Technology Applied Research JPMorganChase New York NY USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Variational quantum algorithms (VQAs) are a broad class of algorithms with many applications in science and industry. Applying a VQA to a problem involves optimizing a parameterized quantum circuit by maximizing or minimizing a cost function. A particular challenge associated with VQAs is understanding the properties of associated cost functions. Having the landscapes of VQA cost functions can greatly assist in developing and testing new variational quantum algorithms, but they are extremely expensive to compute. Reconstructing the landscape of a VQA using existing techniques requires a large number of cost function evaluations, especially when the dimension or the resolution of the landscape is high. To address this challenge, we propose a low-rank tensor-completion-based approach for local landscape reconstruction. By leveraging compact low-rank representations of tensors, our technique can overcome the curse of dimensionality and handle high-resolution landscapes. We demonstrate the power of landscapes in VQA development by showcasing practical applications of analyzing penalty terms for constrained optimization problems and examining the probability landscapes of certain basis states.

关键词： Industries Tensors quantum algorithm Costs Prevention and mitigation Noise Reconstruction algorithms Cost function quantum circuit Testing

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Weighted Feedback-Based quantum algorithm for Excited States Calculation

Weighted Feedback-Based Quantum Algorithm for Excited States...

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quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Salahuddin Abdul Rahman Özkan Karabacak Rafal Wisniewski Department of Electronic Systems Section of Automation and Control Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Drawing inspiration from the Lyapunov control technique for quantum systems, feedback-based quantum al-gorithms have been proposed for calculating the ground states of Hamiltonians. In this work, we consider extending these algorithms to tackle calculating excited states. Inspired by the weighted subspace-search variational quantum eigensolver al-gorithm, we propose a novel weighted feedback-based quantum algorithm for excited state calculation. We show that depending on how we design the weights and the feedback law, we can prepare the pth excited state or lowest energy states up to the pth excited state. Through an application in quantum chemistry, we show the effectiveness of the proposed algorithm, evaluating its efficacy via numerical simulations.

关键词： quantum system quantum algorithm quantum chemistry Stationary state Numerical simulation Energy states

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Noise Removal in IBM Real Time quantum Computer with Deviation quantum algorithm in Comparison with Variational quantum Eigensolver algorithm

Noise Removal in IBM Real Time Quantum Computer with Deviati...

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International Conference on Advances in Information Technology (ICAIT)

作者： P. Venkata Ramana T. Suresh Balakrishnan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The fundamental purpose of this study is to reduce the amount of noise that is produced by IBM quantum computers that are running in real time. This will be accomplished by adopting quantum computing methodologies that are more exact and are drawn from real-world settings. The Constituents and the Methods involved: It is possible to choose from a total of twenty different iterations for both of the groups. On the other hand, the algorithm that constitutes Group 2 is known as the quantum Eigensolver algorithm Variational, and the algorithm that constitutes Group 1 is known as the Deviation quantum algorithm. There are ten iterations in both Group 1 and Group 2, and each of them has a confidence interval of 95% and a G power range of 80%. Both groups are comprised of one another. Observations and discoveries: The Deviation quantum method (32%), which is more accurate in terms of accuracy, is more accurate than the Variational quantum Eigensolver Method (24%), which is less accurate with regard to accuracy. It was found that the hypothesis was accurate after the results of the independent sample T test were analyzed. Furthermore, it is worth noting that the mean number of standard deviations for accuracy detection was two, and the significance value was 0.000 (p<0.05). Due to the fact that this is the case, it is feasible to arrive at the conclusion that the Deviation quantum technique has an accuracy of 32%, which surpasses the Variational quantum's precision Eigensolver method, which is 24%.

关键词： Computers Accuracy quantum algorithm Noise Real-time systems Noise measurement Information technology Standards

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A Novel Pauli Evolutionary quantum algorithm for Combinatorial Optimization

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Chinese Journal of Electronics 2023年第3期19卷 399-402页

作者： Yan Xiong Xiao Liang Fuyou Miao Department of Computer Science and Technology University of Science and Technology of China Hefei China

In this paper, a novel evolutionary quantum algorithm with Pauli mutation PEQA is proposed to solve the combinatorial optimization problem. PEQA utilizes quantum bit strings, quantum gate and Pauli mutation to obtain the best solution with only one individual in a short time. The analysis of Markov chain and empirical experiments on 0/1 knapsack problem are carried out. The results show that PEQA can outperform traditional genetic and quantum evolutionary algorithms.

关键词： quantum algorithm Sociology Evolutionary computation Markov processes Logic gates Genetics Statistics

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