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quantum Byzantine Agreement for Any Number of Dishonest Parties

quantum information processing 2022年第4期21卷 1-11页

作者： Cholvi, Vicent Univ Jaume 1 Dept Llenguatges & Sistemes Informat Campus Rius Sec S-N Castellon de La Plana 12071 Spain

Reaching agreement in the presence of arbitrary faults is a fundamental problem in distributed computation, which has been shown to be unsolvable if one-third of the processes can fail, unless signed messages are used. In this paper, we propose a solution to a variation of the original BA problem, called Detectable Byzantine Agreement (DBA), that does not need to use signed messages. The proposed algorithm uses what we call Q-correlated lists, which are generated by a quantum source device. Once each process has one of these lists, they use them to reach the agreement in a classical manner. Although, in general, the agreement is reached by using in + 1 rounds (where in is the number of processes that can fail), if less than one-third of the processes fail it only needs one round to reach the agreement.

关键词： Byzantine agreement quantum information quantum distribution algorithms quantum communication Q-Correlated lists

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Fast-QTrain: an algorithm for fast training of variational classifiers

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quantum information processing 2022年第5期21卷 1-20页

作者： Dangwal, Siddharth Sharma, Ritvik Bhowmik, Debanjan Indian Inst Technol Delhi Dept Elect Engn Delhi 110016 India Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India

This work proposes a novel algorithm (Fast-QTrain) that enables fast training of variational classifiers. This training speedup is achieved by processing multiple samples, from a classical data set, in parallel during the training process. The proposed algorithm utilizes a quantum RAM along with other quantum circuits for implementing the forward pass. Besides, instead of computing the loss classically, which is the usual practice, we calculate the loss here using a swap test circuit. As a result, our algorithm reduces the training cost of a variational classifier trained for in epochs from the usual O(mN) (which is also the case with most classical machine learning algorithms) to O(N + m log N) where the data set contains N samples. Ignoring the one-time overhead of loading the N training samples into the qRAM, the time complexity per epoch of training is O(log N) in our proposed algorithm, as opposed to O(N) (which is the case for other variational algorithms and most classical machine learning algorithms). By performing quantum-circuit simulations on the Pennylane package, we show fairly accurate training using our proposed algorithm on a popular, classical data set: Fisher's Iris data set of flowers. While we restrict ourselves to binary classification (of samples from classical data sets) in this paper, our algorithm can be easily generalized to carry out multi-class classification. Our proposed algorithm (Fast-QTrain) can also be adapted for any classification ansatz used in the variational circuit as long as the encoding of the classical data into qubits is non-parameterized.

关键词： quantum machine learning Variational algorithm Supervised learning

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A quantum of QUIC: Dissecting Cryptography with Post-quantum Insights 23

A Quantum of QUIC: Dissecting Cryptography with Post-Quantum...

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23rd International-Federation-for-information-processing (IFIP) Networking Conference (IFIP Networking)

作者： Kempf, Marcel Gauder, Nikolas Jaeger, Benedikt Zirngibl, Johannes Carle, Georg Tech Univ Munich Munich Germany

ISBN: (纸本)9798350390605;9783903176638

QUIC is a new network protocol standardized in 2021. It was designed to replace the TCP/ TLS stack and is based on UDP. The most current web standard HTTP / 3 is specifically designed to use QUIC as transport protocol. QUIC claims to provide secure and fast transport with low-latency connection establishment, flow and congestion control, reliable delivery, and stream multiplexing. To achieve the security goals, QUIC enforces the usage of TLS 1.3. It uses authenticated encryption with additional data (AEAD) algorithms to not only protect the payload but also parts of the header. The handshake relies on asymmetric cryptography, which will be broken with the introduction of powerful quantum computers, making the use of post-quantum cryptography inevitable. This paper presents a detailed evaluation of the impact of cryptography on QUIC performance. The high-performance QUIC implementations LSQUIC, quiche, and MsQuic are evaluated under different aspects. We break symmetric cryptography down to the different security features. To be able to isolate the impact of cryptography, we implemented a NOOP AEAD algorithm which leaves plaintext unaltered. We show that QUIC performance increases by 10 to 20% when removing packet protection. The header protection has negligible impact on performance, especially for AES ciphers. We integrate post-quantum cryptographic algorithms into QUIC, demonstrating its feasibility without major changes to the QUIC libraries by using a TLS library that implements post-quantum algorithms. Kyber, Dilithium, and FALCON are promising candidates for post-quantum secure QUIC, as they have a low impact on the handshake duration. algorithms like SPHINCS+ with larger key sizes or more complex calculations significantly impact the handshake duration and cause additional issues in our measurements.

关键词： QUIC Cryptography Performance Evaluation Post-quantum Secure Transport Protocols

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quantum Prototype Clustering: Advancing AI Enhanced Machine Learning 9

Quantum Prototype Clustering: Advancing AI Enhanced Machine ...

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9th International Conference on Signal processing and Communication, ICSC 2023

作者： Bangroo, Ishan Shivansh Kumar, Ravi Department of Computer and Information Science and Engineering University of Florida FL United States Department of Electronics and Communication Engineering Jaypee University of Engineering & Technology M.P. India

ISBN: (纸本)9798350383201

The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing AI techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms. © 2023 IEEE.

关键词： Clustering algorithms

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quantum algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants 36

Quantum Algorithms for Sampling Log-Concave Distributions an...

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36th Conference on Neural information processing Systems (NeurIPS)

作者： Childs, Andrew M. Li, Tongyang Liu, Jin-Peng Wang, Chunhao Zhang, Ruizhe Univ Maryland Dept Comp Sci Joint Ctr Quantum Informat & Comp Sci College Pk MD 20742 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Peking Univ Sch Comp Sci Beijing Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Univ Calif Berkeley Dept Math Berkeley CA USA Univ Calif Berkeley Simons Inst Berkeley CA USA Penn State Univ Dept Comp Sci & Engn University Pk PA USA Univ Texas Austin Dept Comp Sci Austin TX USA

ISBN: (纸本)9781713871088

Given a convex function f : R-d -> R, the problem of sampling from a distribution proportional to e(-f(x)) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants integral(Rd) e(-f(x)) dx. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number kappa and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error epsilon. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity (O) over tilde(kappa(1/2) d) and (O) over tilde(kappa(1/2) d(3/2) /epsilon) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in kappa, d, c over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/epsilon(1-o(1)) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in epsilon.

关键词： Sampling

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QuanTA: Efficient High-Rank Fine-Tuning of LLMs with quantum-Informed Tensor Adaptation 38

QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Chen, Zhuo Dangovski, Rumen Loh, Charlotte Dugan, Owen Luo, Di Soljačić, Marin NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Massachusetts Institute of Technology United States Department of EECS Massachusetts Institute of Technology United States Department of Physics Harvard University United States

We propose quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pretrained language models. By leveraging quantum-inspired methods derived from quantum circuit structures, QuanTA enables efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA)-low-rank approximation may fail for complicated downstream tasks. Our approach is theoretically supported by the universality theorem and the rank representation theorem to achieve efficient high-rank adaptations. Experiments demonstrate that QuanTA significantly enhances commonsense reasoning, arithmetic reasoning, and scalability compared to traditional methods. Furthermore, QuanTA shows superior performance with fewer trainable parameters compared to other approaches and can be designed to integrate with existing fine-tuning algorithms for further improvement, providing a scalable and efficient solution for fine-tuning large language models and advancing state-of-the-art in natural language processing. © 2024 Neural information processing systems foundation. All rights reserved.

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Compendium of Qubit Technologies in quantum Computing 1

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Sebastian, Eby Poonia, Ramesh Chandra Department of Computer Science Assumption College Kerala Changanassery686101 India Karnataka Bangalore560029 India

ISBN: (数字)9789819921003

ISBN: (纸本)9789819920990

quantum computing is information processing based on the principles of quantum mechanics. Qubits are at the core of quantum computing. A qubit is a quantum state where information can be encoded, processed, and readout. Any particle, sub-particle, or quasi-particle having a quantum phenomenon is a possible qubit candidate. Ascendancy in algorithms and coding demands knowledge of the specificities of the inherent hardware. This paper envisages qubits from an information processing perspective and analyses core qubit technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trapped ions

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A New Median Filter Circuit Design Based on Atomic Silicon quantum-Dot for Digital Image processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

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2024 International Conference Optoelectronic information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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Extending Regev's Factoring Algorithm to Compute Discrete Logarithms 15th

Extending Regev's Factoring Algorithm to Compute Discrete Lo...

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15th International Workshop on Post-quantum Cryptography (PQCrypto)

作者： Ekera, Martin Gartner, Joel KTH Royal Inst Technol Stockholm Sweden Swedish Armed Forces Swedish NCSA Stockholm Sweden

ISBN: (纸本)9783031627453;9783031627460

Regev recently introduced a quantum factoring algorithm that may be perceived as a d-dimensional variation of Shor's factoring algorithm. In this work, we extend Regev's factoring algorithm to an algorithm for computing discrete logarithms in a natural way. Furthermore, we discuss natural extensions of Regev's factoring algorithm to order finding, and to factoring completely via order finding. For all of these algorithms, we discuss various practical implementation considerations, including in particular the robustness of the post-processing.

关键词： quantum cryptanalysis Discrete logarithms Factoring

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