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Restricting to the chip architecture maintains the quantum neural network accuracy

quantum information processing 2024年第4期23卷 131-131页

作者： Friedrich, Lucas Maziero, Jonas Univ Fed Santa Maria Ctr Nat & Exact Sci Phys Dept Roraima Ave 1000 BR-97105900 Santa Maria RS Brazil

In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum facet is characterized by a parametrization U, typically derived from the composition of various quantum gates. On the other hand, the classical component involves an optimizer that adjusts the parameters of U to minimize a cost function C. Despite the extensive applications of VQAs, several critical questions persist, such as determining the optimal gate sequence, devising efficient parameter optimization strategies, selecting appropriate cost functions, and understanding the influence of quantum chip architectures on the final results. This article aims to address the last question, emphasizing that, in general, the cost function tends to converge toward an average value as the utilized parameterization approaches a 2-design. Consequently, when the parameterization closely aligns with a 2-design, the quantum neural network model's outcome becomes less dependent on the specific parametrization. This insight leads to the possibility of leveraging the inherent architecture of quantum chips to define the parametrization for VQAs. By doing so, the need for additional swap gates is mitigated, consequently reducing the depth of VQAs and minimizing associated errors.

关键词： quantum neural networks Variational quantum algorithms quantum chip architecture

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Implementation and Analysis of quantum Homomorphic Encryption 13

Implementation and Analysis of Quantum Homomorphic Encryptio...

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13th International Conference on information, Intelligence, Systems and Applications, IISA 2022

作者： Yarter, Maxwell Uehara, Glen Spanias, Andreas Arizona State University SenSIP Center School of Ecee United States

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

Growing interest in the field of quantum computing is fueled by quantum computers projected 'quantum supremacy' in speed and security. The potential for ultra-high speeds may produce a dramatic change in data science, machine learning, analytics, and information processing. This research study will focus on encryption algorithms where quantum computing may affect protocols and deciphering codes. Specifically, homomorphic encryption (HE) enables mathematical operations to be performed on encrypted data without having to decrypt the data in the process. quantum homomorphic encryption (QHE) enables quantum circuits to be performed on encrypted qubits. In this research experience for undergraduates (REU) study, we design quantum circuits to implement QHE on a quantum teleportation circuit. The teleportation algorithm is profiled in terms of performance and complexity and comparative results are provided for encoded versus unencoded circuits. This work serves as a building block for encrypting more complex quantum algorithms such as quantum Neural Networks (QNN). © 2022 IEEE.

关键词： Qubits

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Predicting Stride Length from Acceleration Signals Using Lightweight Machine Learning algorithms

Predicting Stride Length from Acceleration Signals Using Lig...

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International Conference on Internet of Everything and quantum information processing, IEQIP 2023

作者： Dao, To-Hieu Tran, Duc-Nghia Tran, Van-An Vu, Hoang-Dieu Prakash, Kolla Bhanu Tran, Duc-Tan Phenikaa University Yen Nghia Hanoi Viet Nam PRATI A&A Green Phoenix Group JSC Cau Giay Hanoi Viet Nam Graduate University of Sciences and Technology VAST Hanoi Viet Nam Institute of Information Technology VAST Hanoi Viet Nam Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9783031619281

In the field of motion analysis and behavioral movement research, accurately measuring and predicting stride length from acceleration signals plays a critical role. Recent studies have undertaken the development and evaluation of numerous machine learning models to address this issue, focusing on the deployment of lightweight and efficient models on devices with limited computational capabilities, such as microcontrollers. Among the algorithms examined, random forest, k-nearest neighbors, support vector machines, decision trees, and extreme gradient boosting have been selected due to their balance between prediction accuracy and computational lightness. These algorithms not only ensure a high degree of accuracy in predicting stride length from collected acceleration data but also ensure that the models can be deployed directly on smart wearable devices and microcontrollers, extending the practical application of motion monitoring systems. The outcomes of these machine learning models promise to contribute to the development of personalized health monitoring solutions, as well as in applications related to sports and functional rehabilitation, where analyzing and improving gait is extremely important. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Direct measurement of ultrafast temporal wavefunctions

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OPTICS EXPRESS 2021年第13期29卷 19403-19416页

作者： Ogawa, Kazuhisa Okazaki, Takumi Kobayashi, Hirokazu Nakanishi, Toshihiro Tomita, Akihisa Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan Kochi Univ Technol Sch Syst Engn Kochi 7828502 Japan Kyoto Univ Dept Elect Sci & Engn Kyoto 6158510 Japan

The large capacity and robustness of information encoding in the temporal mode of photons is important in quantum information processing, in which characterizing temporal quantum states with high usability and time resolution is essential. We propose and demonstrate a direct measurement method of temporal complex wavefunctions for weak light at a single-photon level with subpicosecond time resolution. Our direct measurement is realized by ultrafast metrology of the interference between the light under test and self-generated monochromatic reference light;no external reference light or complicated post-processing algorithms are required. Hence, this method is versatile and potentially widely applicable for temporal state characterization. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Photons quantum cryptography quantum information processing quantum metrology Spectral phase interferometry for direct field reconstruction Ultrafast measurement

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Exploring the non-Markovian dynamics in depolarizing maps

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Physical Review A 2024年第5期110卷 052209页

作者： Ali Abu-Nada Subhashish Banerjee Vivek Balasaheb Sabale The Centre of Excellence for Applied Research and Training Khalifa Bin Zayed Air College Al Ain United Arab Emirates Department of Physics Department of Chemistry

The non-Markovian depolarizing channel is explored from the perspective of understanding its non-Markovian behavior as well as the occurrence of singularities. The study brings together the various ways to identify and quantify non-Markovianity. This includes dynamical techniques such as quantum information backflow witness, Breuer-Laine-Piilo, Rivas-Huelga-Plenio, and Hall-Cresser-Li-Andersson measures. In addition, geometrical visualization of non-Markovian effects is presented using the variation in the volume of accessible states during dynamical evolution. Further, a trajectory-based visualization of the dynamical map within the parameter space is presented. The trajectories traced during evolution demonstrate the loss of CP divisibility and the emergence of non-Markovianity under systematic variations of the system parameters. The effects of increasing system dimensions and qubit numbers on singularity and non-Markovianity are presented, with an extension of characterization techniques to higher-dimensional systems.

关键词： Open quantum systems & decoherence quantum algorithms & computation quantum computation quantum correlations in quantum information quantum information processing quantum simulation

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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2024 IEEE International Conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

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Convergence of the quantum dynamics framework for optimization algorithm

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quantum information processing 2024年第3期23卷 66-66页

作者： Wang, Fang Wang, Peng Chinese Acad Sci Chengdu Inst Comp Applicat Qunxian South St Chengdu 610213 Sichuan Peoples R China Univ Chinese Acad Sci Yuquan Rd Beijing 100049 Peoples R China Southwest Minzu Univ Sch Comp Sci & Engn Heavy Equipments Rd Chengdu 610041 Sichuan Peoples R China

In this study, the convergence theorem of the quantum dynamic framework (QDF) is given and the convergence of QDF is proved by the imaginary time evolution method in theoretical physics. Convergence analysis has shown that the convergence of QDF is a fast and then slow process;the convergence rate mainly depends on the difference between the optimal solution and the sub-optimal solution. The experimental results verify the theoretical conclusion. This study indicates that quantum dynamics may become a new theoretical paradigm for the study of optimization algorithms and promote the study of the universality theory of optimization algorithms.

关键词： Convergence quantum dynamics framework Wave function Ground state Imaginary time evolution

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Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

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ENERGIES 2023年第24期16卷 8077页

作者： Prokop, Kamil Bien, Andrzej Barczentewicz, Szymon AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Dept Power Elect & Energy Control Syst PL-30059 Krakow Poland

Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper's novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods.

关键词： data compression smart grid communication in power grids real-time operating systems signal processing information theory big data cybersecurity Internet of Things

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quantum algorithms to compute the neighbour list of N-body simulations

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quantum information processing 2024年第2期23卷 61-61页

作者： Combarro, E. F. Rua, I. F. Orts, F. Ortega, G. Puertas, A. M. Garzon, E. M. Univ Oviedo Comp Sci Dept Oviedo Spain Univ Oviedo Math Dept Oviedo Spain Univ Almeria Informat Dept Almeria Spain Univ Almeria Dept Appl Phys Almeria Spain

One of the strategies to reduce the complexity of N-body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.

关键词： quantum computing quantum algorithm Neighbour list N-body simulations

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An educational model of the Deutsch algorithm for secondary school

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EUROPEAN JOURNAL OF PHYSICS 2024年第2期45卷 025302-025302页

作者： Sutrini, Claudio Zuccarini, Giacomo Malgieri, Massimiliano Bondani, Maria Macchiavello, Chiara Univ Pavia Dept Phys I-27100 Pavia Italy Univ Insubria Dept Sci & High Technol I-20100 Como Italy ItalyInstitute Photon & Nanotechnol Inst Photon & Nanotechnol I-22100 Como Italy

In this paper, we present the outline of an educational path to introduce a crucial historical turnpoint of quantum information research-namely the Deutsch algorithm-to secondary school students. We discuss a basic elementarization strategy allowing students to single out and focus on the individual features of quantum mechanics involved in the different steps of the algorithm information processing phase, which can potentially be useful for the educational reconstruction of other algorithms and protocols. The sequence includes the experimental realization on the optical bench of an analogue of the Deutsch algorithm, working with classical coherent light. The educational path was tested both in curricular and out-of-school settings, and preliminary results will be discussed.

关键词： quantum information quantum computation Deutsch algorithm

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