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quantum classifier with parameterized quantum circuit based on the isolated quantum system

The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 21-31页

作者： Shi Jinjing Wang Wenxuan Xiao Zimeng Mu Shuai Li Qin School of Computer Science and Engineering Central South UniversityChangsha 410083China School of Computer Science and School of Cyberspace Science Xiangtan UniversityXiangtan 411301China

It is a critical challenge for quantum machine learning to classify the datasets *** article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensional ***,a model of QC-IQS is presented by creating parameterized quantum circuits(PQCs)based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum ***,a parameterized quantum classification algorithm(QCA)is designed to calculate the classification results by updating the loss function until it ***,the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of *** experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of ***,QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse *** promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.

关键词： quantum classifier quantum classification isolated quantum system parameterized quantum circuit Hamiltonian

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parameterized Hamiltonian Learning With quantum circuit

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 6086-6095页

作者： Shi, Jinjing Wang, Wenxuan Lou, Xiaoping Zhang, Shichao Li, Xuelong Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Hamiltonian learning, as an important quantum machine learning technique, provides a significant approach for determining an accurate quantum system. This paper establishes parameterized Hamiltonian learning (PHL) and explores its application and implementation on quantum computers. A parameterized quantum circuit for Hamiltonian learning is first created by decomposing unitary operators to excite the system evolution. Then, a PHL algorithm is developed to prepare a specific Hamiltonian system by iteratively updating the gradient of the loss function about circuit parameters. Finally, the experiments are conducted on Origin Pilot, and it demonstrates that the PHL algorithm can deal with the image segmentation problem and provide a segmentation solution accurately. Compared with the classical Grabcut algorithm, the PHL algorithm eliminates the requirement of early manual intervention. It provides a new possibility for solving practical application problems with quantum devices, which also assists in solving increasingly complicated problems and supports a much wider range of application possibilities in the future.

关键词： quantum computing quantum circuit quantum system Computers Logic gates Image segmentation quantum state quantum machine learning parameterized hamiltonian learning (PHL) parameterized quantum circuit Hamiltonian learning algorithm image segmentation

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quantum circuit Learning With parameterized Boson Sampling

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1965-1976页

作者： Shi, Jinjing Tang, Yongze Lu, Yuhu Feng, Yanyan Shi, Ronghua Zhang, Shichao Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

A quantum circuit learning approach is studied to carry out the fast-fitting of Gaussian functions. First, a parameterized structure is designed for quantum circuits based on the boson sampling model. And then, the training procedure of exploiting gradient -based optimizations is presented to iteratively update the gradient of the loss function concerning circuit parameters. For efficiency, two kinds of circuit loss, the kernel maximum mean discrepancy and the mean absolute error, are used in the training procedure, which are both competent to achieve quantum circuit learning well. It is significant that the two circuit losses assist in reducing the variance to 2.54 x 10(-6) and 6.91 x 10(-6), respectively. Finally, a kind of quantum circuit fixed structure is developed with the boson sampling model that can decrease the model complexity as the circuit depth d grows. Sets of experiments have been conducted to evaluate the proposed quantum circuit learning scheme, and demonstrate that our parameterized approach is efficient and promising, and it is worth looking forward to solving practical application problems with quantum computers since valid quantum circuits for Gaussian function fast-fitting can be designed indeed.

关键词： quantum circuit Integrated circuit modeling Computers Computational modeling Task analysis Logic gates Data models quantum computing hybrid quantum-classical approach quantum circuit learning boson sampling parameterized boson sampling parameterized quantum circuit gaussian function fitting

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A quantum residual attention neural network for high-precision material property prediction

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quantum INFORMATION PROCESSING 2025年第2期24卷 1-21页

作者： Yang, Qingchuan Zhang, Wenjun Wei, Lianfu Southwest Jiaotong Univ Int Cooperat Res Ctr China Commun & Sensor Network Sch Informat Sci & Technol Informat Quantum Technol Lab Chengdu 610031 Peoples R China Sichuan Zhitu Linghai Intelligent Technol Co Ltd Chengdu 610213 Peoples R China

The rapid advancement of quantum neural networks has led to the application of a range of quantum machine learning algorithms, such as the hybrid quantum convolutional neural network (HQCNN), in various data processing tasks. To further enhance the convergence rate and accuracy of learning from a small number of samples, we introduce a novel model called quantum residual attention neural network (QRANN), which incorporates a quantum residual attention layer (QRAL) to reduce the depth of the quantum circuit and the number of trained parameters. The benefits of this new model are demonstrated through its application to the efficient prediction of material properties based on component optimization. Specifically, we conducted numerical experiments using publicly available alloy material datasets from a hackathon competition to predict the properties of alloy materials based on their composition. The results indicate that the proposed QRANN algorithm exhibits superior performance in terms of training convergence speed, prediction accuracy, and generalization ability compared to HQCNN, QSANN, variational quantum regression (VQR) algorithm, and classical multilayer perceptron. This suggests that QRANN is particularly well-suited for learning from limited datasets. Notably, by introducing a fully parameterized QRAL, QRANN can be implemented with fewer parameters and a lower circuit depth compared to HQCNN, using approximately only 74% and 58% of the parameters and circuit depth used in HQCNN experiments, respectively. Therefore, the proposed algorithm can be feasibly realized using current noisy intermediate-scale quantum devices.

关键词： quantum residual attention neural network quantum machine learning Few-shot learning parameterized quantum circuit Material property prediction

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Design and analysis of a novel quantum-classical hybrid neural network for environmental sound classification

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APPLIED ACOUSTICS 2025年 231卷

作者： She, Tao Shao, Haijian Deng, Xing Jiang, Yingtao Jiangsu Univ Sci & Technol Sch Comp Zhenjiang 212100 Jiangsu Peoples R China Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA

In the field of Environmental Sound Classification (ESC), traditional deep learning methods face significant challenges in processing high-dimensional audio data. In response, this study innovatively proposes a quantum- classical hybrid neural network model-St-HQCNN. This model adeptly integrates the advantages of quantum superposition and quantum entanglement, which are fundamental to quantum computing, thereby significantly enhancing the model's ability to recognize and process complex sound data with greater robustness. Through the design of carefully crafted quantum circuits and misalignment reading mechanisms, St-HQCNN not only demonstrates outstanding classification performance on benchmark datasets such as UrbanSound8K, ESC-10, and ESC-50, achieving accuracy rates of 97.14%, 99.38%, and 93.75%, respectively, but also highlights the tremendous potential of quantum-enhanced neural networks in sound recognition tasks. The experimental results clearly reveal the substantial promise of quantum-assisted neural networks in this domain, laying the foundation for future research and practical applications. This study not only showcases the theoretical innovations of StHQCNN but also focuses on its feasibility for real-world applications. Moving forward, we aim to explore the scalability of the model, address challenges in practical deployment, and advance the widespread use of quantum neural networks in the field of machine learning.

关键词： Hybrid quantum-classical neural network Environmental sound classification Signal processing parameterized quantum circuit

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A Hybrid quantum-Classical Autoencoder Framework for End-to-End Communication Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 806-810页

作者： Zhang, Bolun Zheng, Gan Van Huynh, Nguyen Univ Warwick Sch Engn Coventry CV4 7AL England Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

This letter investigates the application of quantum machine learning to End-to-End (E2E) communication systems in wireless fading scenarios. We introduce a novel hybrid quantum-classical autoencoder architecture that combines parameterized quantum circuits with classical deep neural networks (DNNs). Specifically, we propose a hybrid quantum-classical autoencoder (QAE) framework to optimize the E2E communication system. Our results demonstrate the feasibility of the proposed hybrid system, and reveal that it is the first work that can achieve comparable block error rate (BLER) performance to classical DNN-based and conventional channel coding schemes, while significantly reducing the number of trainable parameters. Additionally, the proposed QAE exhibits steady and superior BLER convergence over the classical autoencoder baseline.

关键词： Qubit Vectors Autoencoders Receivers quantum circuit Transmitters quantum state quantum entanglement Logic gates Wireless communication End-to-End communication systems quantum machine learning parameterized quantum circuit fading channel and deep learning

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quantum Neural Networks: A Path to Lower Emissions Through Fuel Consumption Prediction in Shipping

Quantum Neural Networks: A Path to Lower Emissions Through F...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chien, S.F. Hermans, Julien J.M. Kana, Austin A. Zarakovitis, Charilaos C. Zavvos, Stathis Lim, H.S. Strategic ICT MIMOS Berhad Kuala Lumpur Malaysia Delft University of Technology Dept. of Maritime and Transport Technology Delft Netherlands Axon Logic Dept. of Applied Mathematics 7 Dekelias Athens14122 Greece VLTN BV De Keyserlei 58-60 Bus 19 Antwerp2018 Belgium Faculty of Engineering and Technology Multimedia University Melaka Malaysia

ISBN: (纸本)9798350368741

This paper proposes quantum Neural Networks (QNNs) as a data-driven approach for predicting fuel consumption. We utilize various layer architecture designs available in the Torchquantum framework, including both entangled and non-entangled circuit designs. In general, QNNs can achieve comparable Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with significantly fewer trainable parameters. Neither pure QNNs nor hybrid QNN models exhibit the underfitting tendencies seen in classical neural networks (CNNs). Notably, one of the most significant findings of this work is that hybridizing or"dressing" the quantum circuit leads to substantial improvements in RMSE and MAPE for pure QNNs. These promising results suggest potential optimizations for reducing emissions in green shipping. © 2025 IEEE.

关键词： Classical neural network digital twin fuel consumption modeling NISQ devices parameterized quantum circuit quantum neural network

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Resource-Efficient Adaptive Variational quantum Algorithm for Combinatorial Optimization Problems

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ADVANCED quantum TECHNOLOGIES 2025年第0期

作者： Wu, Sheng-Yao Song, Yan-Qi Li, Run-Ze Qin, Su-Juan Wen, Qiao-Yan Gao, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China China Acad Informat & Commun Technol Beijing 100191 Peoples R China China Elect Technol Grp Corp Res Inst 15 Beijing 100083 Peoples R China Beijing Univ Posts & Telecommun Natl Engn Res Ctr Disaster Backup & Recovery Beijing 100876 Peoples R China

Variational quantum algorithms (VQAs) are quantum-classical hybrid algorithms that are promising for the near future. The quantum Approximate Optimization Algorithm (QAOA) is a representative VQA for solving combinatorial optimization problems. However, the parameterized quantum circuit (PQC) of QAOA still has room for improvement. The existing method, called ADAPT-QAOA, has improved the PQC of QAOA, but the circuit depth remains deep. A Resource-Efficient Adaptive VQA (RE-ADAPT-VQA) that utilizes gates from a new gate pool is proposed to construct a PQC. RE-ADAPT-VQA incrementally integrates parameterized quantum gates based on the gradient until the predefined stopping criteria are satisfied, and a rollback mechanism is proposed to ensure that the circuit remains shallow. The algorithm is experimentally simulated to solve Max-Cut problem and the maximum independent set problem. The results show that RE-ADAPT-VQA significantly reduces circuit depth, single-qubit gates, and CNOT gates compared to existing methods, while maintaining the same level of energy error.

关键词： max-cut maximum independent set parameterized quantum circuit variational quantum algorithm

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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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Fine-tuned Variational quantum Classifiers for Cyber Attacks Detection based on parameterized quantum circuits and Optimizers 48

Fine-tuned Variational Quantum Classifiers for Cyber Attacks...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Rahman, Md Abdur Akter, Mst Shapna Miller, Emily Timofti, Bogdan Shahriar, Hossain Masum, Mohammad Wu, Fan Univ West Florida Dept Intelligent Syst & Robot Pensacola FL 32514 USA Univ West Florida Dept Comp Sci Pensacola FL USA Univ West Florida Ctr Cybersecur Pensacola FL USA San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Tuskegee Univ Dept Comp Sci Tuskegee AL 36088 USA

ISBN: (纸本)9798350376975;9798350376968

Recent investigations into quantum Machine Learning (QML) techniques have unveiled methodologies that accelerate training in established machine learning models to provide an alternative for capturing complex patterns. This study focuses on implementing a practical QML Algorithm, Variational quantum Classification (VQC) for cybersecurity dataset so that detecting anomalies can be improved and faster by reducing number of attributes log(2) M while training the model using Qiskit. Also, we study quantum algorithms to understand how it impacts on cyber datasets to detect anomalies in a improved way as it follows logarithms in the dimensionality reduction of quantum states which opens new horizons to quantum big data applications. Most importantly, we aim to also investigate the impact of various parameterized quantum circuits on VQC using quantum data as quantum states encoded by the cyber security dataset, NSL-KDD. In this research, we train VQC with various structures and parameters of quantum circuits as well as optimizers to adjust parameters of quantum circuits (ansatz) to minimize the objective function values so as to improve accuracy of the model in which quantum circuit, EfficientSU2, along with optimizer, COBYLA, outperforms the accuracy than other circuits and optimizers which shows great potential for improving cybersecurity systems. The research could effectively bridge in the gap between theory and implementation based quantum machine learning on cybersecurity systems.

关键词： quantum machine learning variational quantum classifier parameterized quantum circuit optimizer

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