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14th International Conference on Computer and Automation Engineering (ICCAE)

作者： Mishra, Shraddha Tsai, Chi-Yi Tamkang Univ Dept Elect & Comp Engn New Taipei Taiwan

ISBN: (纸本)9781665483797

In this paper, we present a novel quantum neural network (QNN) algorithm enhanced with transfer learning to perform multi-class image classification. The proposed QNN extracts quantum image encoding measurements through the quantum state tomography framework and passes the sampled features through the classical neural network architecture to the proposed learnable parameterized quantum circuit (PQC) followed by gradient update via quantum backpropagation. We benchmark three different PQCs to demonstrate that our proposed algorithm outperforms similar classical CNN architecture in test accuracy on CIFAR10 and MNIST datasets. Present results more prominently establish the success of PQC designs which will be further used in the design of 2D quantum convolutional neural network (QCNN).

关键词： quantum machine learning parameterized quantum circuit quantum neural network image classification

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Interpreting variational quantum models with active paths in parameterized quantum circuits

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷 025067页

作者： Lee, Kyungmin Jeon, Hyungjun Lee, Dongkyu Kim, Bongsang Bang, Jeongho Kim, Taehyun Seoul Natl Univ Dept Comp Sci & Engn Seoul 08826 South Korea LG Elect Quantum AI Dept AI Lab CTO Seoul 06772 South Korea Elect & Telecommun Res Inst Daejeon 34129 South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul 08826 South Korea Seoul Natl Univ Interuniv Semicond Res Ctr Seoul 08826 South Korea Seoul Natl Univ Inst Comp Technol Seoul 08826 South Korea Seoul Natl Univ Inst Appl Phys Seoul 08826 South Korea

Variational quantum machine learning (VQML) models based on parameterized quantum circuits (PQC) have been expected to offer a potential quantum advantage for machine learning (ML) applications. However, comparison between VQML models and their classical counterparts is hard due to the lack of interpretability of VQML models. In this study, we introduce a graphical approach to analyze the PQC and the corresponding operation of VQML models to deal with this problem. In particular, we utilize the Stokes representation of quantum states to treat VQML models as network models based on the corresponding representations of basic gates. From this approach, we suggest the notion of active paths in the networks and relate the expressivity of VQML models with it. We investigate the growth of active paths in VQML models and observe that the expressivity of VQML models can be significantly limited for certain cases. Then we construct classical models inspired by our graphical interpretation of VQML models and show that they can emulate or outperform the outputs of VQML models for these cases. Our result provides a new way to interpret the operation of VQML models and facilitates the interconnection between quantum and classical ML areas.

关键词： quantum machine learning quantum advantage parameterized quantum circuit variational quantum machine learning models

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Variational quantum semi-supervised classifier based on label propagation

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Chinese Physics B 2023年第7期32卷 279-289页

作者：侯艳艳李剑陈秀波叶崇强 College of Information Science and Engineering ZaoZhuang UniversityZaozhuang 277160China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China Information Security Center State Key Laboratory Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China

Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data *** paper proposes a quantum semi-supervised classifier based on label *** the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)*** this method,a locally parameterized quantum circuit is created to reduce the parameters required in the ***,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum *** demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test *** work opens a new path to quantum machine learning based on graphs.

关键词： semi-supervised learning variational quantum algorithm parameterized quantum circuit

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Towards an efficient variational quantum algorithm for solving linear equations

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Communications in Theoretical Physics 2024年第11期76卷 54-65页

作者： WenShan Xu Ri-Gui Zhou YaoChong Li XiaoXue Zhang College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China

Variational quantum algorithms are promising methods with the greatest potential to achieve quantum advantage,widely employed in the era of noisy intermediate-scale quantum *** study presents an advanced variational hybrid algorithm(EVQLSE)that leverages both quantum and classical computing paradigms to address the solution of linear equation ***,an innovative loss function is proposed,drawing inspiration from the similarity measure between two quantum *** function exhibits a substantial improvement in computational complexity when benchmarked against the variational quantum linear ***,a specialized parameterized quantum circuit structure is presented for small-scale linear systems,which exhibits powerful expressive *** rigorous numerical analysis,the expressiveness of this circuit structure is quantitatively assessed using a variational quantum regression algorithm,and it obtained the best score compared to the ***,the expansion in system size is accompanied by an increase in the number of parameters,placing considerable strain on the training process for the *** address this challenge,an optimization strategy known as quantum parameter sharing is introduced,which proficiently minimizes parameter volume while adhering to exacting precision ***,EVQLSE is successfully implemented on a quantum computing platform provided by IBM for the resolution of large-scale problems characterized by a dimensionality of 220.

关键词： quantum computing variational quantum algorithm systems of linear equations parameterized quantum circuit

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Network intrusion detection based on variational quantum convolution neural network

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12743-12770页

作者： Gong, Changqing Guan, Weiqi Zhu, Hongsheng Gani, Abdullah Qi, Han Shenyang Aerosp Univ Sch Comp Sci Shenyang 110000 Peoples R China Univ Malaysia Sabah Fac Comp & Informat Labuan Int Campus Labuan 87000 Malaysia

With the rapid development of quantum machine learning (QML), quantum convolutional neural networks (QCNN) have been proposed and shown advantages in classification problems. An intrusion detection system (IDS) based on the QML method is proven to have higher accuracy than IDS based on the traditional machine learning (ML) method. However, the multiple convolution pooling operations of QCNN will cause the loss of valuable data features, resulting in a large error in the final measurement results. In this paper, we design an IDS model of QCNN based on a variational quantum neural network (VQNN), which can effectively reduce data feature loss and improve detection accuracy. We compare this model with traditional ML models such as artificial neural network (ANN), logistic regression (LR), K-nearest neighbor (KNN) algorithm, support vector machine (SVM), and decision tree (DT). Experiment results show that the accuracy of our proposed model is 94.51%, which is higher than other classical IDS models.

关键词： quantum machine learning quantum convolution neural network parameterized quantum circuit Intrusion detection

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Implementing Graph-Theoretic Feature Selection by quantum Approximate Optimization Algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2364-2377页

作者： Li, YaoChong Zhou, Ri-Gui Xu, RuiQing Luo, Jia Hu, WenWen Fan, Ping Shanghai Maritime Univ Res Ctr Intelligent Informat Proc & Quantum Intel Shanghai 201306 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China East China Jiaotong Univ Coll Informat Engn Nanchang 330013 Jiangxi Peoples R China

Feature selection plays a significant role in computer science;nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k-subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn(2)) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.

关键词： Feature extraction quantum computing Optimization Approximation algorithms quantum state Search problems quantum circuit Feature selection graph theory parameterized quantum circuit quantum approximation optimization algorithm quantum computing

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Hybrid quantum-Classical Neural Networks for Downlink Beamforming Optimization

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第11期23卷 16498-16512页

作者： Zhang, Juping Zheng, Gan Koike-Akino, Toshiaki Wong, Kai-Kit Burton, Fraser A. UCL Dept Elect & Elect Engn London WC1E 7JE England Univ Warwick Sch Engn Coventry CV4 7AL England Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Martlesham Heath Appl Res Dept British Telecom Ipswich IP5 3RE England

This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters;while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices.

关键词： quantum computing Neural networks Array signal processing Optimization Wireless communication Machine learning algorithms Qubit quantum machine learning parameterized quantum circuit hybrid quantum and classical neural network beamforming

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Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network

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PHYSICA SCRIPTA 2024年第5期99卷 055121-055121页

作者： Hou, Dong Che, Xuanxuan Li, Feifei Dong, Yumin Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

Methane as a renewable energy source has become a hot topic in recent years. Methane is a bioenergy source produced during the anaerobic digestion of organic waste, and the anaerobic digestion process must be monitored and controlled to produce the required amount of methane in a stable manner. Mathematical modeling is used to simulate digester operation to predict the biogas production from anaerobic digestion, to avoid reactor loading or performance degradation, and to ensure efficient operation of the system. In this paper, a quantum Convolutional Reconstruction Gated Recurrent Neural Network is proposed. The original data features are extracted by convolutional neural network to reduce the dimensionality and retain the information, the parameterized quantum circuit is integrated in the gating recurrent unit, and the quantum reset gate and quantum update gate are constructed. The information extracted by the Convolution Neural networks is input into the quantum gated recurrent neural network, and the quantum storage unit integrates the information into the hidden layer state, thus processing the hidden layer state information more efficiently. The experimental results show that the prediction accuracy of the A quantum Convolution Reconstructed Gated Recurrent Neural Network is improved from 81.95 to 88.21%, and the MAE value is reduced from 54.53% to 37.38%.

关键词： quantum computings anaerobic digestion parameterized quantum circuit gated recurrent units

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On a Piecewise Linear Function Approximation for quantum Computation 19

On a Piecewise Linear Function Approximation for Quantum Com...

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Asia Pacific Conference on circuits and Systems (APCCAS)

作者： Okazaki, Hideaki Shonan Inst Technol Grad Sch Elect & Informat Engn Dept Informat Comp Sci Course Field Fac Informat 1-1-25 Tsujido Nishikaigan Fujisawa Kanagawa 2518511 Japan

ISBN: (纸本)9798350381191

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in [0, 1] of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao : an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

关键词： parameterized quantum circuit approximating linear functions Yao open source Julia package

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Modified Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics Event Classification 2

Modified Layerwise Learning for Data Re-uploading Classifier...

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IEEE International Conference on quantum Computing and Engineering (QCE) / quantum Week

作者： Muten, Eraraya Ricardo Yusuf, Togan Tlimakhov Tomut, Andrei Voicu Inst Teknol Bandung Quantum Technol Lab Bandung Indonesia Ankara Univ Dept Elect & Elect Engn Ankara Turkey Babes Bolyai Univ Fac Phys Cluj Napoca Romania

ISBN: (纸本)9781665416917

This paper aims to demonstrate the use of modified layerwise learning on a data-reuploading classifier, where the parameterized quantum circuit will be used as a quantum classifier to classify the SUSY dataset. We managed to produce a better result using this approach compared to the previous related research with fewer qubits. We obtained an AUC of 0.849 on a testing dataset with 5000 training and testing samples, trained and tested using a state-vector simulator. We also tested to run the circuit on Rigetti's Aspen-9 quantum processing unit provided by AWS using the already optimized parameter to predict 2000 samples of the test dataset and obtained an AUC of 0.830.

关键词： classification HEP data analysis parameterized quantum circuit quantum computing quantum machine learning

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