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22nd IEEE International Symposium on Parallel and Distributed processing with Applications, ISPA 2024

作者： Zhao, Hongjian Tao, Yunting Kong, Fanyu Zhang, Guoyan Zhang, Hanlin Yu, Jia Shandong University School of Software Jinan China Binzhou Polytechnic College of Information Engineering Binzhou China Shandong University Shandong Sansec Information Technology Co. Ltd School of Software Jinan China Shandong University School of Cyber Science and Technology Qingdao China Qingdao University College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331509712

Lattice-based Post-quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographic computations in blockchain systems, and polynomial multiplication, a time-consuming operation for decryption, is one of the factors limiting blockchain efficiency. This paper proposes a novel distributed computing algorithm for polynomial multiplication, applicable in blockchain decryption. By splitting polynomials into lower-degree terms and delegating tasks to distributed nodes, our approach reduces computation time. A novel verification strategy based on the Karatsuba algorithm ensures result accuracy. The experimental results demonstrate that our proposed scheme improves the execution efficiency of NTT and INTT operations by approximately 47.8% and 52.4%, and reduces Kyber decryption time by up to 23.5%. © 2024 IEEE.

关键词： Encryption algorithms

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Research on GISC Image Reconstruction Algorithm Based on Area Array CCD Image Sensor 3

Research on GISC Image Reconstruction Algorithm Based on Are...

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3rd International Signal processing, Communications and Engineering Management Conference, ISPCEM 2023

作者： Shen, Ziyi Li, Meixuan Yang, Zhipeng Wang, Honglin Fan, Xiaofei Yang, Feng Jilin Engineering Normal University Changchun China Institute for Interdisciplinary Quantum Information Technology Jilin Engineering Normal University Changchun China

ISBN: (纸本)9798350307023

Ghost imaging sparsity constraints (GISC) spectral camera can acquire both spatial and spectral information of surface features, and has significant application prospects in the field of satellite remote sensing. This article uses a planar CCD image sensor for photoelectric detection, and then reconstructs multispectral images through image reconstruction algorithms. The article describes the theoretical foundation, related technologies, and image reconstruction algorithms of GISC. Reconstruct the three-dimensional spectral image of the target through single exposure detection of the speckle field and pre calibrated measurement matrix. The research results have advantages such as high energy utilization rate, fast information acquisition efficiency, and the ability to obtain three-dimensional data information with a single exposure. © 2023 IEEE.

关键词： Energy utilization

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quantum circuit autoencoder

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Physical Review A 2024年第3期109卷 032623-032623页

作者： Jun Wu Hao Fu Mingzheng Zhu Haiyue Zhang Wei Xie Xiang-Yang Li School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Department of Computer Science University of Illinois Urbana-Champaign Urbana-Champaign Illinois 61801 USA Hefei National Laboratory University of Science and Technology of China Hefei 230088 China

A quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoders, we introduce the quantum circuit autoencoder (QCAE) model to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks, and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum circuits. These suggest that our algorithm is potentially applicable to other information processing tasks for quantum circuits.

关键词： Artificial neural networks quantum algorithms & computation quantum information theory

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Neural-network-encoded variational quantum algorithms

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Physical Review Applied 2024年第1期21卷 014053-014053页

作者： Jiaqi Miao Chang-Yu Hsieh Shi-Xin Zhang School of Physics Zhejiang University Hangzhou Zhejiang 310000 China Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences Zhejiang University Hangzhou 310058 China Tencent Quantum Laboratory Tencent Shenzhen Guangdong 518057 China

We introduce a general framework called neural-network- (NN) encoded variational quantum algorithms (VQAs), or NNVQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NNVQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. Combining the strengths of NN and parameterized quantum circuits, NNVQA can accelerate the training process of VQAs and handle a broad family of related problems with varying input parameters with the pretrained NN. To concretely illustrate the merits of NNVQA, we present results on a NN variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models in one and two dimensions. Our results demonstrate that NNVQE is able to estimate the ground-state energies of parameterized Hamiltonians with high precision without fine tuning, and significantly reduce the overall training cost to estimate ground-state properties across the phases of the given Hamiltonian. We also employ an active learning strategy to further increase the training efficiency while maintaining prediction accuracy. These encouraging results demonstrate that NNVQAs offer an alternative hybrid quantum-classical paradigm to utilize NISQ resources for solving more realistic and challenging computational problems.

关键词： quantum algorithms quantum information processing quantum simulation Artificial neural networks Machine learning Variational approach

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Emulating quantum computing with optical matrix multiplication

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APL PHOTONICS 2024年第10期9卷

作者： Koni, Mwezi Bezuidenhout, Hadrian Nape, Isaac Univ Witwatersrand Sch Phys Private Bag 3 ZA-2050 Johannesburg South Africa

Optical computing harnesses the speed of light to perform vector-matrix operations efficiently. It leverages interference, a cornerstone of quantum computing algorithms, to enable parallel computations. In this work, we interweave quantum computing with classical structured light by formulating the process of photonic matrix multiplication using quantum mechanical principles such as state superposition and subsequently demonstrate a well-known algorithm, namely, Deutsch-Jozsa's algorithm. This is accomplished by elucidating the inherent tensor product structure within the Cartesian transverse degrees of freedom of light, which is the main resource for optical vector-matrix multiplication. To this end, we establish a discrete basis using localized Gaussian modes arranged in a lattice formation and demonstrate the operation of a Hadamard gate. Leveraging the reprogrammable and digital capabilities of spatial light modulators, coupled with Fourier transforms by lenses, our approach proves adaptable to various algorithms. Therefore, our work advances the use of structured light for quantum information processing. (c) 2024 Author(s).

关键词： Fourier transforms

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Correlation thresholds for effective composite pulse quantum error mitigation

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Physical Review A 2024年第5期110卷 052614-052614页

作者： Ido Kaplan Haim Suchowski Yaron Oz School of Electrical Engineering The Iby and Aladar Fleischman Faculty of Engineering Tel-Aviv University Tel-Aviv 69978 Israel Raymond and Beverly Sackler School of Physics and Astronomy Tel-Aviv University Tel-Aviv 69978 Israel

Composite pulse segmentation has emerged as a promising error-mitigation technique for a wide range of physical systems. In recent years, composite schemes were applied as mitigation strategies for quantum information processing and quantum computing. However, most of these strategies assume full error correlation between segments, which can result in gates with worse fidelity performance compared to noncomposite gates. In our research, we investigate how error correlations impact the fidelity of quantum gates within the composite segmentation framework. In our study, we prove the existence of a critical correlation threshold, above which the composite pulse method significantly enhances both the mean value and variance of the fidelity. To gain deeper insights, we analyze various properties of the threshold in the realm of integrated photonics, including the effects of geometrical variations and the limit where the number of segments approaches infinity. We numerically explore diverse scenarios, showcasing different aspects of the critical threshold within the photonic quantum gates framework. These findings contribute to new pathways of error-mitigation strategies and their implications in quantum information processing.

关键词： quantum algorithms & computation quantum control quantum error correction quantum simulation

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Grover's Algorithm for Data Lake Optimization Queries

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 568-576页

作者： Cherradi, Mohamed EL Haddadi, Anass Abdelmalek Essaadi Univ Data Sci & Competit Intelligence Team DSCI ENSAH Tetouan Morocco

the use of No-SQL databases is one of the potential options for storing and processing big data lakes. However, searching for large data in No-SQL databases is a complex and time-consuming task. Further, information retrieval from big data management suffers in terms of execution time. To reduce the execution time during the search process, we propose a fast and suitable approach based on the quantum Grover algorithm, which represents one of the best-known approaches for searching in an unstructured database and resolves the unsorted search query in O (root n) time complexity. To assess our proposal, a comparative study with linear and binary search algorithms was conducted to prove the effectiveness of Grover's algorithms. Then, we perform extensive experiment evaluations based on ibm_qasm_simulator for searching one item out of eight using Grover's search algorithm based on three qubits. The experiments outcomes revealed encouraging results, with an accuracy of 0.948, well in accordance with the theoretical result. Moreover, a discussion of the sensitivity of Grover's algorithm through different iterations was carried out. Then, exceeding the optimal number of iterations round (pi/4 root N), induces low accuracy of the marked state. Furthermore, the incorrect selection of this parameter can outline the solution.

关键词： Big data data management information retrieval quantum computing

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quantum natural gradient without monotonicity

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Physical Review A 2024年第2期110卷 022439页

作者： Toi Sasaki Hideyuki Miyahara Graduate School of Information Science and Technology Hokkaido University Hokkaido Sapporo 060-0814 Japan

The natural gradient (NG) is an information-geometric optimization method that plays a crucial role, especially in the estimation of parameters for machine learning models like neural networks. To apply NG to quantum systems, the quantum natural gradient (QNG) was introduced and utilized for noisy intermediate-scale devices. Additionally, a mathematically equivalent approach to QNG, known as the stochastic reconfiguration method, has been implemented to enhance the performance of quantum Monte Carlo methods. It is worth noting that these methods are based on the symmetric logarithmic derivative (SLD) metric, which is one of the monotone metrics. So far, monotonicity has been believed to be a guiding principle to construct a geometry in physics. In this paper we propose generalized QNG by removing the condition of monotonicity. Initially, we demonstrate that monotonicity is a crucial condition for conventional QNG to be optimal. Subsequently, we provide analytical and numerical evidence showing that nonmonotone QNG outperforms conventional QNG based on the SLD metric in terms of convergence speed.

关键词： Machine learning Optimization problems quantum algorithms & computation quantum computation quantum information processing quantum parameter estimation quantum simulation

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Hardware-efficient and accurately frequency offset compensation based on feedback structure and polar coordinates processing

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OPTICAL AND quantum ELECTRONICS 2023年第2期55卷 1-11页

作者： Zhang, Xuefeng Ju, Cheng Wang, Dongdong Zhao, Yuanfan Xie, Peng Liu, Na Qingdao Univ Coll Elect Informat Qingdao Peoples R China CETC Acad Network & Commun Shijiazhuang 050081 Peoples R China Sci & Technol Commun Networks Lab Shijiazhuang 050081 Peoples R China

Compared with off-line digital signal processing (DSP) verification, achieving the expected performance and hardware efficiency in application specific integrated circuit (ASIC) is more critical for DSP to reduce power consumption and cost. The performance of traditional frequency offset compensation algorithms based on feedforward structure is strongly dependent on the accuracy of frequency offset estimation. Therefore, frequency offset calculation and loop filtering implemented in ASIC or field programmable gate array (FPGA) usually choose high word-width fixed-point or floating-point operations, which increase the consumption of logical resources and reduce the clock frequency that hardware logic can achieve. This work proposes a frequency offset compensation scheme based on polar coordinate processing and feedback structure, and a pre-decision-based angle differential estimator is used to estimate the residual frequency offset. In the proposed feedback structure, the frequency offset estimator is realized by a simple accumulator, and the input of the accumulator is the residual frequency offset or its scaling, which will reduce the requirement for the accuracy of the residual frequency offset estimator. The offline verification results show that the performance of the proposed algorithm is close to that of the traditional algorithm, but the proposed algorithm has lower logic resource consumption and higher clock frequency in hardware implementation based on FPGA. The performance of the proposed method is evaluated in real-time through 10-Gbps data rate polarization-multiplexed quadrature phase shift keying modulation after 20-km standard single-mode fiber transmission. Compared with offline processing in MATLAB (R), no hardware implementation penalty is observed.

关键词： Real-time FPGA Frequency offset compensation Feedback structure Pre-decision-based angle differential estimator

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Post quantum cryptography: alternative solutions to hard problems for security

Post quantum cryptography: alternative solutions to hard pro...

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作者： Emerole, Kelechi Chukwunonyerem Newcastle University

学位级别：博士

Post quantum cryptography are defined as public key crypto algorithms whose pub- lic keys are generated from hard computational problems that are complex to solve in polynomial time by a quantum computer given worst case instances. The hard problems which have been proven to be quantum resistant include the shortest vec- tor problem of lattices, the syndrome decoding problem of certain error correcting codes and the isomorphism of polynomial problem of multivariate quadratic poly- nomials. Solutions to these problems have been proposed which in turn have impact on the security and storage cost of such algorithms to protect information systems in the future. In this thesis, alternative solutions are proposed which are based on robust and complex vector space mappings. Firstly, Dimensionality mapping is pro- posed to reduce the basis into its linear independent vectors at low dimensionality by constructing a collapse function as an optimization problem. This optimization problem can be solved on the condition that a projection of the basis vectors from the High dimensional space to low dimensional manifold would have nearly orthogonal constitution. These eliminates the need for pre-processing using Gram-Schmidt Or- thogonalization process. Implementing this approach on a channel basis, showed an improved BER performance over the Lenstra-Lenstra-Lovatsz algorithm for about 1db and 4db in the 4 × 4 and 6 × 6 uncoded system using 4QAM constellation. Secondly, the solution of the syndrome decoding problem is generalized to codes associated with the totally non-negative Grassmannian. The solution was reduced to an instance of finding a subset of the Plucker coordinates with the minimum Grassmann distance from the subspace containing the encrypted message symbols. Furthermore, bounds where derived which showed that the complexity scales up on the size of the Plucker coordinates. In addition, experimental results on decoding failure probability and complexity based on row op

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