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Unsupervised beyond-standard-model event discovery at the LHC with a novel quantum autoencoder

quantum MACHINE INTELLIGENCE 2025年第1期7卷 1-19页

作者： Duffy, Callum Hassanshahi, Mohammad Jastrzebski, Marcin Malik, Sarah UCL Phys & Astron Gower St London WC1E 6BT England

This study explores the potential of unsupervised anomaly detection for identifying physics beyond the standard model that may appear at proton collisions at the Large Hadron Collider. We introduce a novel quantum autoencoder circuit ansatz that is specifically designed for this task and demonstrates superior performance compared to previous approaches. To assess its robustness, we evaluate the quantum autoencoder on various types of new physics 'signal' events and varying problem sizes. Additionally, we develop classical autoencoders that outperform previously proposed quantum autoencoders but remain outpaced by the new quantum ansatz, despite its significantly reduced number of trainable parameters. Finally, we investigate the properties of quantum autoencoder circuits, focusing on entanglement and magic. We introduce a novel metric in the context of parameterised quantum circuits, stabiliser 2-R & eacute;nyi entropy to quantify magic, along with the previously studied Meyer-Wallach measure for entanglement. Intriguingly, both metrics decreased throughout the training process along with the decrease in the loss function. This appears to suggest that models preferentially learn parameters that reduce (but not minimise) these metrics. This study highlights the potential utility of quantum autoencoders in searching for physics beyond the standard model at the Large Hadron Collider and opens exciting avenues for further research into the role of entanglement and magic in quantum machine learning more generally.

关键词： quantum autoencoder High-energy physics Anomaly detection Magic Entanglement entropy

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quantum-Classical autoencoder Architectures for End-to-End Radio Communication

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IEEE ACCESS 2025年 13卷 82181-82192页

作者： Tabi, Zsolt I. Bako, Bence Nagy, Daniel T. R. Vaderna, Peter Kallus, Zsofia Haga, Peter Zimboras, Zoltan Eotvos Lorand Univ Fac Informat H-1053 Budapest Hungary HUN REN Wigner Res Ctr Phys Quantum Comp & Informat Grp H-1121 Budapest Hungary Eotvos Lorand Univ Fac Sci H-1053 Budapest Hungary Ericsson Res H-1117 Budapest Hungary Univ Helsinki QTF Ctr Excellence Dept Phys Helsinki 00100 Finland

End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints - including data re-uploading methods, encoding schemes, and core layer structures.

关键词： autoencoders Encoding Radio communication Adaptation models Symbols Neural networks Computer architecture Receivers quantum circuit Machine learning quantum machine learning quantum autoencoder radio communication

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Clustering and enhanced classification using a hybrid quantum autoencoder

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quantum SCIENCE AND TECHNOLOGY 2022年第1期7卷

作者： Srikumar, Maiyuren Hill, Charles D. Hollenberg, Lloyd C. L. Univ Melbourne Sch Phys Parkville Vic 3010 Australia Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify-and classically represent-their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states-which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

关键词： quantum machine learning quantum computing quantum autoencoder

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quantum Patch-Based autoencoder for Anomaly Segmentation 5

Quantum Patch-Based Autoencoder for Anomaly Segmentation

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2024 International Conference on quantum Computing and Engineering

作者： Madeira, Maria Francisca Poggiali, Alessandro Lorenz, Jeanette Miriam Ludwig Maximilians Univ Munchen Munich Germany Fraunhofer IKS Munich Germany Univ Pisa Pisa Italy

ISBN: (纸本)9798331541378

quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, allowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.

关键词： quantum Machine Learning Anomaly Segmentation quantum autoencoder

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quantum neural network autoencoder and classifier applied to an industrial case study

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quantum MACHINE INTELLIGENCE 2022年第2期4卷 1-13页

作者： Mangini, Stefano Marruzzo, Alessia Piantanida, Marco Gerace, Dario Bajoni, Daniele Macchiavello, Chiara Univ Pavia Dipartimento Fis Via Bassi 6 I-27100 Pavia Italy Ist Nazl Fis Nucl Sez Pavia Via Bassi 61 I-27100 Pavia Italy Eni SpA Via Emilia 1 I-20097 San Donato Milanese Italy Univ Pavia Dipartimento Ingn Ind & Informaz Via Ferrata 1 I-27100 Pavia Italy CNR INO Largo E Fermi 6 I-50125 Florence Italy

quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is relevant to develop algorithms that are useful for actual industrial processes. In this work, we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.

关键词： quantum machine learning Industrial case study quantum autoencoder Classification quantum data analysis

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Applying quantum autoencoders for time series anomaly detection

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quantum MACHINE INTELLIGENCE 2025年第1期7卷 1-21页

作者： Frehner, Robin Stockinger, Kurt Zurich Univ Appl Sci Zurich Switzerland

Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition, or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60-230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.

关键词： quantum computing quantum machine learning quantum autoencoder Time series anomaly detection

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On compression rate of quantum autoencoders: Control design, numerical and experimental realization

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AUTOMATICA 2023年 147卷

作者： Ma, Hailan Huang, Chang-Jiang Chen, Chunlin Dong, Daoyi Wang, Yuanlong Wu, Re-Bing Xiang, Guo-Yong Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Chinese Acad Sci Univ Sci & Technol China Key Lab Quantum Informat Hefei 230026 Peoples R China CAS Ctr Excellence Quantum Informat & Quantum Phys Hefei Peoples R China Nanjing Univ Sch Management & Engn Dept Control & Syst Engn Nanjing 210093 Peoples R China Griffith Univ Ctr Quantum Dynam Brisbane Qld 4111 Australia Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states.(c) 2022 Published by Elsevier Ltd.

关键词： quantum autoencoder quantum control Learning control Compression rate

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quantum image compression with autoencoders based on parameterized quantum circuits

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quantum INFORMATION PROCESSING 2024年第2期23卷 41-41页

作者： Wang, Hengyan Tan, Jing Huang, Yixiao Zheng, Wenqiang Zhejiang Univ Sci & Technol Dept Phys Hangzhou 310023 Peoples R China Zhejiang Univ Technol Zhejiang Prov Key Lab Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Sci Collaborat Innovat Ctr Quantum Precis Measurement Hangzhou 310023 Peoples R China

The analysis and processing of digital images play a vital role in information processing. However, the pixel-based operations on images often lead to significant complexity as the image data grow rapidly. Encoding images into a quantum system and leveraging the principles of superposition and entanglement offer a chance to alleviate the challenges. A further improvement in efficiency is promising by combining quantum image processing with machine learning algorithms. Here a quantum autoencoder is trained to compress the image data into a lower-dimensional space using a hybrid quantum-classical control approach. The optimization of the parameterized quantum circuit involves the measurement of simple observables, alleviating the computational burden associated with the calculation of cost functions and gradients. We applied our quantum autoencoder to compress the MNIST handwritten digit dataset. The results exhibit the feasibility and effectiveness of the quantum compression approach. This work highlights the potential application of quantum neural networks in achieving high-efficiency quantum image processing.

关键词： quantum image compression quantum autoencoder Parameterized quantum circuit

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Schmidt quantum compressor

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quantum SCIENCE AND TECHNOLOGY 2025年第3期10卷

作者： Araujo, Israel F. Oh, Hyeondo Rodriguez-Briones, Nayeli A. Park, Daniel K. Yonsei Univ Dept Stat & Data Sci Seoul 03722 South Korea Univ Fed Pernambuco Dept Eletron & Sistemas BR-50740550 Recife Pernambuco Brazil Univ Fed Pernambuco Ctr Informat BR-50740560 Recife Pernambuco Brazil Univ Calif Berkeley Miller Inst Basic Res Sci Berkeley CA 94720 USA Tech Univ Wien Atominst Stadionallee 2 A-1020 Vienna Austria Yonsei Univ Dept Appl Stat Seoul 03722 South Korea

This work introduces the Schmidt quantum compressor, an innovative approach to quantum data compression that leverages the principles of Schmidt decomposition to encode quantum information efficiently. In contrast to traditional variational quantum autoencoders, which depend on stochastic optimization and face challenges such as shot noise, barren plateaus, and non-convex optimization landscapes, our deterministic method substantially reduces the complexity and computational overhead of quantum data compression. We evaluate the performance of the compressor through numerical experiments, demonstrating its ability to achieve high fidelity in quantum state reconstruction compared to variational quantum algorithms. Furthermore, we demonstrate the practical utility of the Schmidt quantum compressor in one-class classification tasks.

关键词： quantum computing quantum data compression quantum machine learning quantum autoencoder one-class classification

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quantum deep learning-based anomaly detection for enhanced network security

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quantum MACHINE INTELLIGENCE 2024年第1期6卷 1-18页

作者： Hdaib, Moe Rajasegarar, Sutharshan Pan, Lei Deakin Univ Fac Sci Engn & Built Environm Sch Informat Technol Geelong Vic 3220 Australia

Identifying and mitigating aberrant activities within the network traffic is important to prevent adverse consequences caused by cyber security incidents, which have been increasing significantly in recent times. Existing research mainly focuses on classical machine learning and deep learning-based approaches for detecting such attacks. However, exploiting the power of quantum deep learning to process complex correlation of features for anomaly detection is not well explored. Hence, in this paper, we investigate quantum machine learning and quantum deep learning-based anomaly detection methodologies to accurately detect network attacks. In particular, we propose three novel quantum auto-encoder-based anomaly detection frameworks. Our primary aim is to create hybrid models that leverage the strengths of both quantum and deep learning methodologies for efficient anomaly recognition. The three frameworks are formed by integrating the quantum autoencoder with a quantum one-class support vector machine, a quantum random forest, and a quantum k-nearest neighbor approach. The anomaly detection capability of the frameworks is evaluated using benchmark datasets comprising computer and Internet of Things network flows. Our evaluation demonstrates that all three frameworks have a high potential to detect the network traffic anomalies accurately, while the framework that integrates the quantum autoencoder with the quantum k-nearest neighbor yields the highest accuracy. This demonstrates the promising potential for the development of quantum frameworks for anomaly detection, underscoring their relevance for future advancements in network security.

关键词： quantum machine learning quantum autoencoder quantum anomaly detection quantum computing

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