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  • Open Access


    Pancreatic Cancer Data Classification with Quantum Machine Learning

    Amit Saxena1, Smita Saxena2,*

    Journal of Quantum Computing, Vol.5, pp. 1-13, 2023, DOI:10.32604/jqc.2023.044555

    Abstract Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different… More >

  • Open Access


    Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

    Ghada Abdulsalam1, Souham Meshoul2,*, Hadil Shaiba3

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 761-779, 2023, DOI:10.32604/iasc.2023.032262

    Abstract Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk… More >

  • Open Access


    Lifetime Prediction of LiFePO4 Batteries Using Multilayer Classical-Quantum Hybrid Classifier

    Muhammad Haris1,*, Muhammad Noman Hasan1 , Abdul Basit2, Shiyin Qin1

    Journal of Quantum Computing, Vol.3, No.3, pp. 89-95, 2021, DOI:10.32604/jqc.2021.016390

    Abstract This article presents a multilayer hybrid classical-quantum classifier for predicting the lifetime of LiFePO4 batteries using early degradation data. The multilayer approach uses multiple variational quantum circuits in cascade, which allows more parameters to be used as weights in a single run hence increasing accuracy and provides faster cost function convergence for the optimizer. The proposed classifier predicts with an accuracy of 92.8% using data of the first four cycles. The effectiveness of the hybrid classifier is also presented by validating the performance using untrained data with an accuracy of 84%. We also demonstrate More >

  • Open Access


    Learning Unitary Transformation by Quantum Machine Learning Model

    Yi-Ming Huang1, Xiao-Yu Li1,*, Yi-Xuan Zhu1, Hang Lei1, Qing-Sheng Zhu2, Shan Yang3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 789-803, 2021, DOI:10.32604/cmc.2021.016663

    Abstract Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientific research and improving data processing. How to efficiently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing. It can be described as learning or approximating a unitary operator. Since the success of the hybrid-based quantum machine learning model proposed in recent years, we investigate to apply the techniques from QML to tackle this problem. Based on the Choi–Jamiołkowski isomorphism in quantum computing, we transfer… More >

  • Open Access


    Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning

    Daniel Sierra-Sosa1,*, Juan D. Arcila-Moreno2, Begonya Garcia-Zapirain3, Adel Elmaghraby1

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1849-1861, 2021, DOI:10.32604/cmc.2021.013196

    Abstract Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification More >

  • Open Access


    Quantum Hierarchical Agglomerative Clustering Based on One Dimension Discrete Quantum Walk with Single-Point Phase Defects

    Gongde Guo1, Kai Yu1, Hui Wang2, Song Lin1, *, Yongzhen Xu1, Xiaofeng Chen3

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1397-1409, 2020, DOI:10.32604/cmc.2020.011399

    Abstract As an important branch of machine learning, clustering analysis is widely used in some fields, e.g., image pattern recognition, social network analysis, information security, and so on. In this paper, we consider the designing of clustering algorithm in quantum scenario, and propose a quantum hierarchical agglomerative clustering algorithm, which is based on one dimension discrete quantum walk with single-point phase defects. In the proposed algorithm, two nonclassical characters of this kind of quantum walk, localization and ballistic effects, are exploited. At first, each data point is viewed as a particle and performed this kind of… More >

  • Open Access


    Quantum Generative Adversarial Network: A Survey

    Tong Li1, Shibin Zhang1, *, Jinyue Xia2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

    Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits More >

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