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On new PageRank computation methods using quantum computing

quantum information processing 2023年第3期22卷 138-138页

作者： Chapuis-Chkaiban, Theodore Toffano, Zeno Valiron, Benoit Cent Supelec Dept Comp Sci 3 Rue Joliot Curie F-91190 Gif Sur Yvette France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay CNRS Cent Supelec Lab Methodes FormellesENS Paris Saclay F-91190 Gif Sur Yvette France

In this paper we propose several new quantum computation algorithms as an original contribution to the domain of PageRank algorithm theory, Spectral Graph Theory and quantum Signal processing. We first propose an application to PageRank of the HHL quantum algorithm for linear equation systems. We then introduce one of the first quantum-Based algorithms to perform a directed Graph Fourier Transform with a low gate complexity. After proposing a generalized PageRank formulation, based on ideas stemming from Spectral Graph Theory, we show how our quantum directed graph Fourier Transform can be applied to compute our generalized version of the PageRank.

关键词： quantum computing PageRank Graph Fourier transform Spectral graph theory information theory

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Photonic Simulation of Majorana-Based Jones Polynomials

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Physical Review Letters 2024年第23期133卷 230603-230603页

作者： Jia-Kun Li Kai Sun Ze-Yan Hao Jia-He Liang Si-Jing Tao Jiannis K. Pachos Jin-Shi Xu Yong-Jian Han Chuan-Feng Li CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 China School of Physics and Astronomy University of Leeds Leeds LS2 9JT United Kingdom Hefei National Laboratory University of Science and Technology of China Hefei 230088 China

By braiding non-Abelian anyons it is possible to realize fault-tolerant quantum algorithms through the computation of Jones polynomials. So far, this has been an experimentally formidable task. In this Letter, a photonic quantum system employing two-photon correlations and nondissipative imaginary-time evolution is utilized to simulate two inequivalent braiding operations of Majorana zero modes. The resulting amplitudes are shown to be mathematically equivalent to Jones polynomials. The high fidelity of our optical platform allows us to distinguish between a wide range of links, such as Hopf links, Solomon links, Trefoil knots, Figure Eight knots and Borromean rings, through determining their corresponding Jones polynomials. Our photonic quantum simulator represents a significant step towards executing fault-tolerant quantum algorithms based on topological quantum encoding and manipulation.

关键词： Geometric & topological phases quantum information processing quantum simulation

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An algorithm based on quantum phase estimation for the identification of patterns

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quantum information processing 2024年第5期23卷 194-194页

作者： Ntalaperas, Dimitris Kalogeropoulos, Andreas Konofaos, Nikos Aristotle Univ Thessaloniki Comp Sci Dept Thessaloniki 54124 Greece

The quantum phase estimation algorithm has been utilized by a variety of quantum algorithms, most notably Shor's algorithm, to obtain information regarding the period of a function that is appropriately encoded into a unitary operator. In many cases, it is desired to estimate whether a specific state exhibits a certain pattern quickly. In this paper, we exhibit a methodology based on the QPE algorithm to identify certain patterns. In particular, starting from a properly encoded state, we demonstrate how to construct unitary operators whose eigenvectors correspond to states with proper diagonals. QPE will then output an eigenphase equal to zero with certainty for these states, thereby identifying this class of matrices. For partial matches, our algorithm, based on the tolerance threshold used, will show areas of high similarity, and it will outperform classical ones when the number of mismatches defined by the tolerance is significantly low when compared to the size of the diagonal.

关键词： quantum pattern matching quantum phase estimation quantum computing Shor's algorithm

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A survey on quantum data mining algorithms: challenges, advances and future directions

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quantum information processing 2024年第3期23卷 74-74页

作者： Qi, Han Wang, Liyuan Gong, Changqing Gani, Abdullah Shenyang Aerosp Univ Sch Comp Sci 37 Daoyi South Ave Shenyang 110000 Peoples R China Univ Malaya Fac Comp & Informat Technol Kuala Lumpur 50603 Malaysia

Data mining has reached a state that is difficult to break through, while the scale of data is growing rapidly, due to the lack of traditional computing power and limited data storage space. Efficient and accurate extraction of valuable information from massive data has become a challenge. Researchers have combined quantum computing with data mining to address this problem, hence the concept of quantum data mining has emerged. The fundamental tenets of quantum physics are adhered to for information transmission and computing operations in quantum data mining, which use the states of minuscule particles to represent and process information. quantum data mining are based on the characteristics of quantum computing, such as superposition and entanglement, which make the ability of computational and information extraction effectively improved. The paper discusses and summarizes the relevant literature on quantum data mining in recent 3 years. After introducing relevant basic concepts of quantum computing, quantum data mining is presented in five aspects: quantum data classification, quantum data clustering, quantum dimensionality reduction, quantum association rules, quantum linear regression, and quantum causal analysis. These approaches, based on quantum computing, offer new perspectives and tools for handling complex data mining tasks. In conclusion, the development of quantum data mining is promising and crucial to overcome the difficulties associated with large-scale data mining.

关键词： quantum data mining quantum computing Big data Future development

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Systematic literature review: quantum machine learning and its applications

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COMPUTER SCIENCE REVIEW 2024年 51卷

作者： Peral-Garcia, David Cruz-Benito, Juan Garcia-Penalvo, Francisco Jose Univ Salamanca Fac Sci Expert Syst & Applicat Lab ESALAB Plaza Caidos S-N Salamanca 37008 Spain IBM TJ Watson Res Ctr IBM Quantum Yorktown Hts NY 10598 USA Univ Salamanca Res Inst Educ Sci Dept Comp & Automat GRIAL Res Grp Paseo Canalejas 169 Salamanca 37008 Spain

quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault -tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or ansatzs. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k -nearest neighbor model, and classica

关键词： quantum machine learning quantum computing Systematic literature review

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quantum resonant dimensionality reduction

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Physical Review Research 2025年第1期7卷 013007-013007页

作者： Fan Yang Furong Wang Xusheng Xu Pan Gao Tao Xin ShiJie Wei Guilu Long Beijing Academy of Quantum Information Sciences Beijing 100193 China State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China China North Vehicle Research Institute Beijing 100072 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China Collaborative Innovation Center of Quantum Matter Beijing 100084 China

quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing the consumption of quantum resources is the key to achieving quantum advantage. Here, we propose a quantum resonant dimensionality reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data and accelerate the quantum machine learning algorithms. After QRDR, the dimension of input data N can be reduced into the desired scale R, and the effective information of the original data will be preserved correspondingly, which will reduce the computational complexity of subsequent quantum machine learning algorithms or quantum storage. QRDR operates with polylogarithmic time complexity and reduces the error dependency from the order of 1/ε3 to the order of 1/ε, compared to existing algorithms. Meanwhile, by avoiding quantum phase estimation, the consumed qubits in QRDR is independent on error ε. Therefore, compared with existing algorithms, our algorithm has achieved the optimal performance in terms of time complexity and space complexity. We demonstrate the performance of our algorithm combining with two types of quantum classifiers, quantum support vector machines and quantum convolutional neural networks, for classifying underwater detection targets and quantum many-body phase, respectively. The simulation results indicate that reduced data extremely improved the processing efficiency following the application of QRDR. As quantum machine learning continues to advance, our algorithm has the potential to be utilized in a variety of computing fields.

关键词： Dimensionality reduction

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History and metrology applications of a game-changing technology: digital holography [Invited]

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION 2022年第2期39卷 A29-A43页

作者： Trolinger, James D. Mansoor, Mohammad M. Metrolaser Inc 22941 Mill Creek Dr Laguna Hills CA 92653 USA

In digital holography (DH), information in the hologram is recorded and stored in digital format in discrete bits. Like its parent, holography, DH evolved over many years with periods of dormancy and revival. Almost abandoned, multiple times, unanticipated events or developments in separate industries revived it with explosive, quantum jumps, making it useful and popular to a wide audience. Although its history has been treated in many papers and books, the field is dynamic and constantly providing new opportunities. Having been born long before low-cost, fast, powerful digital computers and digital detectors were available, DH was confined to the academic world, where practical applications and commercial opportunities were few if any. Consumer demand that led to low-cost personal computers, high-resolution digital cameras, supporting software, and related products changed the situation drastically by providing every potential researcher affordable, powerful hardware and software needed to apply image processing algorithms and move DH to new practical application levels. In this paper, as part of the sixtieth anniversary of off-axis holography, we include a brief introduction to the fundamentals of DH and examine the history and evolution of DH during its periods of rise and fall. We summarize many new emerging techniques, applications, and potential future applications along with additional details for metrological examples from the authors' research. (c) 2021 Optical Society of America

关键词： Digital holographic imaging Holographic interferometry Holographic optical elements Image processing Image processing algorithms Phase imaging

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Individually Addressed quantum Gate Interactions Using Dynamical Decoupling

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PRX quantum 2024年第3期5卷 030321-030321页

作者： M.C. Smith A.D. Leu M.F. Gely D.M. Lucas Parks Road Oxford OX1 3PU United Kingdom

A leading approach to implementing small-scale quantum computers has been to use laser beams, focused to micron spot sizes, to address and entangle trapped ions in a linear crystal. Here we propose a method to implement individually addressed entangling gate interactions, but driven by microwave fields, with a spatial resolution of a few microns, corresponding to 10−5 microwave wavelengths. We experimentally demonstrate the ability to suppress the effect of the state-dependent force using a single ion, and find the required interaction introduces 3.7(4)×10−4 error per emulated gate in a single-qubit benchmarking sequence. We model the scheme for a 17-qubit ion crystal, and find that any pair of ions should be addressable with an average crosstalk error of approximately 10−5.

关键词： quantum algorithms & computation quantum communication, protocols & technology quantum control quantum entanglement quantum gates quantum information architectures & platforms quantum information processing quantum information with trapped ions

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Parallel Segment Entanglement Swapping 1

Parallel Segment Entanglement Swapping

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1st International Conference on quantum Communications, Networking, and Computing, QCNC 2024

作者： He, Binjie Loke, Seng W. Zhang, Dong College of Computer and Data Science Fuzhou University Fuzhou China School of Information Technology Deakin University Melbourne Australia Zhicheng College Fuzhou University Fuzhou China

ISBN: (纸本)9798350366778

In the noisy intermediate-scale quantum era, scientists are trying to improve the entanglement swapping success rate by researching anti-noise technology on the physical level, thereby obtaining a higher generation rate of long-distance entanglement. However, we may improve the generation rate from another perspective, which is studying an efficient entanglement swapping strategy. This paper analyzes the challenges faced by existing entanglement swapping strategies, including the node allocation principle, time synchronization, and processing of entanglement swapping failure. We present Parallel Segment Entanglement Swapping (PSES) to solve these problems. The core idea of PSES is to segment the path and perform parallel entanglement swapping between segments to improve the generation rate of long-distance entanglement. We construct a tree-like model as the carrier of PSES and propose heuristic algorithms called Layer Greedy and Segment Greedy to transform the path into a tree-like model. Moreover, we realize the time synchronization and design the on-demand retransmission mechanism to process entanglement swapping failure. The experiments show that PSES performs superiorly to other entanglement swapping strategies, and the on-demand retransmission mechanism can reduce the average entanglement swapping time by 80% and the average entanglement consumption by 80%. © 2024 IEEE.

关键词： quantum communication

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Scalable approach to quantum simulation via projection-based embedding

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Physical Review A 2024年第2期109卷 022418-022418页

作者： Alexis Ralli Michael Williams de la Bastida Peter V. Coveney Centre for Computational Science Department of Chemistry University College London London WC1H 0AJ United Kingdom UCL Centre for Advanced Research Computing Gower Street London WC1E 6BT United Kingdom Informatics Institute University of Amsterdam Amsterdam 1098 XH Netherlands

Owing to the computational complexity of electronic structure algorithms running on classical digital computers, the range of molecular systems amenable to simulation remains tightly circumscribed even after many decades of work. Many believe quantum computers will transcend such limitations although in the current era the size and noise of these devices militates against significant progress. Here we describe a chemically intuitive approach that permits a subdomain of a molecule's electronic structure to be calculated accurately on a quantum device, while the rest of the molecule is described at a lower level of accuracy using density functional theory running on a classical computer. We demonstrate that this approach produces improved results for molecules that cannot be simulated fully on current quantum computers but which can be resolved classically at a cheaper level of approximation. The algorithm is tunable, so that the size of the quantum simulation can be adjusted to run on available quantum resources. Therefore, as quantum devices become larger, this method will enable increasingly large subdomains to be studied accurately.

关键词： quantum algorithms quantum algorithms & computation quantum computation quantum information processing quantum simulation

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