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Analysis of a Programmable quantum Annealer as a Random Number Generator

IEEE TRANSACTIONS ON information FORENSICS AND SECURITY 2024年 19卷 3636-3643页

作者： Pelofske, Elijah Los Alamos Natl Lab CCS 3 Informat Sci Los Alamos NM 87545 USA

quantum devices offer a highly useful function - that is generating random numbers in a non-deterministic way since the measurement of a quantum state is not deterministic. This means that quantum devices can be constructed that generate qubits in a uniform superposition and then measure the state of those qubits. If the preparation of the qubits in a uniform superposition is unbiased, then quantum computers can be used to create high entropy, secure random numbers. Typically, preparing and measuring such quantum systems requires more time compared to classical pseudo random number generators (PRNGs) which are inherently deterministic algorithms. Therefore, the typical use of quantum random number generators (QRNGs) is to provide high entropy secure seeds for PRNGs. quantum annealing (QA) is a type of analog quantum computation that is a relaxed form of adiabatic quantum computation and uses quantum fluctuations in order to search for ground state solutions of a programmable Ising model. Here we present extensive experimental random number results from a D-Wave 2000Q quantum annealer, totaling over 20 billion bits of QA measurements, which is significantly larger than previous D-Wave QA random number generator studies. Current quantum annealers are susceptible to noise from environmental sources and calibration errors, and are not in general unbiased samplers. Therefore, it is of interest to quantify whether noisy quantum annealers can effectively function as an unbiased QRNG. The amount of data that was collected from the quantum annealer allows a comprehensive analysis of the random bits to be performed using the NIST SP 800-22 Rev 1a testsuite, as well as min-entropy estimates from NIST SP 800-90B. The randomness tests show that the generated random bits from the D-Wave 2000Q are biased, and not unpredictable random bit sequences. With no server-side sampling post-processing, the 1 microsecond annealing time measurements had a min-entropy of 0.824.

关键词： Annealing quantum annealing Generators Qubit Hardware Computational modeling Servers quantum computing random number generator Ising model min-entropy chimera graph transverse-field Ising model D-Wave 2000Q

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Towards Atomic MIMO Receivers

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第3期43卷 659-673页

作者： Cui, Mingyao Zeng, Qunsong Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

The advancement of Rydberg atoms in quantum information technology is driving a paradigm shift from classical radio-frequency (RF) receivers to Rydberg atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external electromagnetic fields, Rydberg atomic receivers are capable of realizing more precise radio-wave measurements than RF receivers to support high-performance wireless communication and sensing. Although the atomic receiver is developing rapidly in quantum-physics domain, its integration with wireless communications is at a nascent stage. In particular, systematic methods to enhance communication performance through this integration are yet to be discovered. Motivated by this observation, we propose in this paper to incorporate Rydberg atomic receivers into multiple-input-multiple-output (MIMO) communication, a prominent 5G technology, as the first attempt on implementing atomic MIMO receivers. To begin with, we provide a comprehensive introduction on the principles of Rydberg atomic receivers and build on them to design the atomic MIMO receivers. Our findings reveal that signal detection of atomic MIMO receivers corresponds to a non-linear biased phase retrieval (PR) problem, as opposed to the linear Gaussian model adopted in classical MIMO systems. Then, to recover signals from this non-linear model, we modify the Gerchberg-Saxton (GS) algorithm, a typical PR solver, into a biased GS algorithm to solve the biased PR problem. Moreover, we propose a novel Expectation-Maximization GS (EM-GS) algorithm to cope with the unique Rician distribution of the biased PR model. Our EM-GS algorithm introduces a high-pass filter constructed by the ratio of Bessel functions into the iteration procedure of GS, thereby improving the detection accuracy without sacrificing the computational efficiency. Finally, the effectiveness of the devised algorithms and the feasibility of atomic MIMO receivers are demonstrated by theoretical analysis and numerica

关键词： Receivers Atomic measurements Rydberg atoms Radio frequency Sensors Electromagnetic fields quantum sensing Energy states Symbols Signal processing algorithms Rydberg atomic receivers multiple-input-multiple-output (MIMO) quantum sensing signal detection

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Enhancing quantum support vector machines through variational kernel training

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quantum information processing 2023年第10期22卷 1-18页

作者： Innan, N. Khan, M. A. Z. Panda, B. Bennai, M. Hassan II Univ Casablanca Fac Sci Ben Msick Quantum Phys & Magnetism Team LPMC Casablanca Morocco Zaiku Grp Ltd Liverpool England Univ Witwatersrand Sch Comp Sci & Appl Math Robot Autonomous Intelligence Learning Lab RAIL 1 Jan Smuts Ave ZA-2000 Johannesburg Gauteng South Africa Indian Inst Sci Educ & Res IISER Berhampur Odisha India

We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research.

关键词： quantum machine learning quantum support vector machine Kernel quantum variational algorithm Classification

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Parameter optimization in decoy-state phase-matching quantum key distribution

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quantum information processing 2023年第10期22卷 1-11页

作者： Wang, Lu Dong, Qin Jiao, Rongzhen Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

The phase-matching quantum key distribution (PM-QKD) protocol has been widely researched since it was proposed. In this paper, the performance of asymmetric PM-QKD protocol is discussed and the efforts of statistical fluctuation and source error on asymmetric PM-QKD protocol are analyzed through numerical simulations. In the case of limited data sets, system parameters need to be optimized to increase the key rate. However, traditional exhaustive traversal or local search algorithms cannot meet the time requirement of real-time communication. With the development of machine learning, using machine learning for parameter optimization has been widely applied in various disciplines. This paper uses recurrent neural network (RNN) to predict the optimization parameters of asymmetric PM-QKD. The results show that RNN can quickly and accurately predict optimization parameters, which can provide a reference for future real-time QKD networks.

关键词： Phase-matching quantum key distribution Asymmetric channels Recurrent neural network Parameter optimization

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Efficient algorithm for full-state quantum circuit simulation with DD compression while maintaining accuracy

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quantum information processing 2023年第11期22卷 1-25页

作者： Song, Yuhong Sha, Edwin Hsing-Mean Zhuge, Qingfeng Xu, Rui Wang, Han East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China

With the development of noisy intermediate-scale quantum machines, quantum processors show their supremacy in specific applications. To better understand the quantum behavior and verify larger quantum bit (qubit) algorithms, simulation on classical computers becomes crucial. However, as the simulated number of qubits increases, the full-state simulation suffers exponential memory increment for state vector storing. In order to compress the state vector, some existing works reduce the memory by data encoding compressors. Nevertheless, the memory requirement remains massive. Meanwhile, others utilize compact decision diagrams (DD) to represent the state vector, which only demands linear memory size. However, the existing DD-based simulation algorithm possesses many redundant calculations that require further exploration. Besides, the traditional normalization-based nodes merging method of DD amplifies the side influences of approximate error. Therefore, to tackle the above challenges, in this paper, we first fully explore the redundancies in the recursive-based DD simulation (RecurSim) algorithm. Inspired by the regularities of the quantum circuit model, a scale-based simulation (ScaleSim) algorithm is proposed, which removes plenty of unnecessary computations. Furthermore, to eliminate the influences of approximate error, we propose a new pre-check DD building method, namely PCB, which maintains the accuracy of DD representation and produces more memory saving. Comprehensive experiments show that our method achieves up to 24124.2x acceleration and 3.2x10(7)x memory reduction than traditional DD-based methods on quantum algorithms while maintaining the representation accuracy.

关键词： quantum circuit simulation Decision diagram Algorithm optimization Error mitigation

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An efficient quantum algorithm for simulating polynomial dynamical systems

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quantum information processing 2024年第3期23卷 1-22页

作者： Surana, Amit Gnanasekaran, Abeynaya Sahai, Tuhin Raytheon Technol Res Ctr Aerothermal & Intelligent Syst 411 Silver Lane East Hartford CT 06118 USA Raytheon Technol Res Ctr Aerothermal Intelligent Syst 2855 Telegraph Ave Berkeley CA 94705 USA SRI Int Appl Sci Lab 333 Ravenswood Ave Menlo Pk CA 94025 USA

In this paper, we present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary (finite) degree on quantum platforms. Ordinary differential equations (ODEs) and partial differential equations (PDEs) arise extensively in science and engineering applications. Examples of ODE models include mechanics of rigid bodies, molecular dynamics, chemical kinetics, and epidemiology. Nonlinear PDEs arise in fluid dynamics, combustion, weather forecasting, structural mechanics, plasma dynamics, and finance to name a few. In practice, it is challenging to simulate such equations on classical computers due to high dimensionality, stiffness arising from multiple spatial/temporal scales, nonlinearities, and chaotic dynamics. Typically, high performance computing is used to mitigate computational challenges and involves approximations for tractability. For sparse n-dimensional linear ODEs, quantum algorithms have been developed which can produce a quantum state proportional to the solution in poly(log(n))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textsf {poly}(\log (n))$$\end{document}) time using the quantum linear systems algorithm (QLSA). Recently, this framework was extended to systems of nonlinear ODEs with quadratic polynomial vector fields by applying Carleman linearization that enables the embedding of the quadratic system into an approximate linear form. A detailed complexity analysis was conducted which showed significant computational advantage under certain conditions. We present an extension of this algorithm to deal with systems of nonlinear ODEs with k-th degree polynomial vector fields for arbitrary (finite) values of k. The steps involve: (1) mapping the k-th degree polynomial ODE to a higher-dimensional quadratic polyno

关键词： Polynomial differential equations Carleman linearization quantum computing Exponential speedup

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Three quantum neural network architectures for multi-class image classification 3

Three quantum neural network architectures for multi-class i...

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3rd International Conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Qu, Guangkai Yu, Xianglong Wang, Zhimin Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks. © 2025 SPIE.

关键词： quantum electronics

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A high-capacity and robust steganography algorithm for quantum images

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CHINESE JOURNAL OF PHYSICS 2023年 85卷 89-103页

作者： Khan, Mubashar Rasheed, Amer Lahore Univ Management Sci Sch Sci & Engn Dept Math Opposite Sect U Opposite Sect U DHA Cantt 54792 Pakistan

The improved flexible representation of a quantum image (IFRQI) employs an effective method to encode grayscale image information in a normalized quantum state vector, which allows for accurate retrieval of image information through projective measurements. In this paper, we propose a high-capacity and robust steganography algorithm based on the IFRQI model. We embed the grayscale information of a 2(n) x2(n) sized secret image in a 2(n+1) x2(n+1) sized cover image using controlled rotations. The secret image is divided into four image planes, each with a bit depth of 2. For each image plane, an array of angle values encoding the 2-bit color information is prepared. The encoded information is then embedded in the IFRQI state of the cover image using controlled rotations determined by the key K, which is only known to the operator. The secret image extraction algorithm is the inverse process of the embedding algorithm, which requires the inverse key K '. Analyses of the embedding capacity, time complexity, and visual effects reveal that the proposed steganography algorithm is comparable with some state-of-the-art algorithms.

关键词： quantum image processing quantum image steganography Embedding capacity quantum image scrambling Erbium-doped fiber laser chaotic system

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quantum Computing Aided Quality Control and Defect Detection in Laser Melting Deposition Manufacturing

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SPIN 2025年

作者： Zheng, Gaoxiang Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

An innovative additive manufacturing process called laser melting deposition (LMD) has many benefits for precisely generating complex structures. However, process optimization, defect detection and quality control are difficulties that LMD faces despite its potential. The manual inspection and traditional algorithms used in traditional defect identification and quality control procedures are time-consuming, computationally costly and prone to errors in high-dimensional datasets. This research examines the integration of defect detection and quality control methods in LMD production. information from various sensors, including optical, laser, pressure and temperature sensors, are used to track the LMD process. To guarantee consistency and correctness in the dataset, the initial stage entails pre-processing the gathered data using cleaning and min-max normalization techniques. Feature extraction is performed using Principal Component Analysis (PCA), which lowers the data's dimensionality while keeping the crucial details required for a successful analysis. An approach called Dynamic Artificial Rabbits optimized quantum support vector machine (DARO-QSVM) is used to identify quality control parameters and identify flaws in the LMD process;the suggested methodology seeks to increase the accuracy (95.36%) of defect detection, and optimize process parameters in real-time. This study demonstrates how high-dimensional data problems and the dynamic, nonlinear character of LMD could be resolved with quantum-based technologies, providing a viable way forward for improvements in manufacturing quality control in the future.

关键词： Laser melting deposition quality control defect detection quantum computing quantum simulated tabu search optimization

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Elementarizing quantum algorithms: clarification of the internal structure and preliminary learning outcomes

Elementarizing quantum algorithms: clarification of the inte...

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International Conference of the International-Research-Group-on-Physics-Teaching (GIREP) on Effective Learning in Physics from Contemporary Physics to Remote Settings

作者： Zuccarini, Giacomo Malgieri, Massimiliano Sutrini, Claudio Macchiavello, Chiara Univ Pavia Dept Phys Via Bassi 6 I-27100 Pavia Italy

Introducing elements of quantum information and computation in the secondary school curriculum is a trend which has very recently emerged in physics education. In this paper we describe a tentative elementarization scheme for the information processing phase of quantum algorithms, and report on a preliminary evaluation of its feasibility on Italian selfselected secondary school students in distance learning. While the test was conducted on a very small sample in special conditions, this work of clarification promoted a consistent understanding of the algorithmic structure in informational terms and, at least partially, in physical ones. The feasibility test had for us a positive outcome, which led to refinements of the approach and further tests, also on curricular teaching, which were performed from 2022 onwards.

关键词： quantum optics

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