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Diabetes Type 2: Poincaré Data Preprocessing for quantum machine learning

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Computers, Materials & Continua 2021年第5期67卷 1849-1861页

作者： Daniel Sierra-Sosa Juan D.Arcila-Moreno Begonya Garcia-Zapirain Adel Elmaghraby Department of Computer Science and Engineering University of LouisvilleLouisvilleKYUSA Apolo Scientific Computing Center Universidad EAFITMedellínColombia eVida Research Group University of DeustoBilbaoSpain

quantum machine learning(QML)techniques have been recently attracting massive *** reported applications usually employ synthetic or well-known *** of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational quantum Classifier(VQC),which development seems *** 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 *** pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ *** including feature selection techniques and geometrical transformations,enhanced quantum state preparation is ***,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the *** results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus *** used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.

关键词： quantum machine learning data preprocessing stokes parameters Poincarésphere

Explainable Heart Disease Prediction Using Ensemble-quantum machine learning Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 761-779页

作者： Ghada Abdulsalam Souham Meshoul Hadil Shaiba College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Nowadays,quantum machine learning is attracting great interest in a wide range ofﬁelds due to its potential superior performance and *** massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcareﬁ*** disease seriously threa-tens human health since it is the leading cause of death *** machine learning methods can propose effective solutions to predict heart disease and aid in early *** this study,an ensemble machine learning model based on quantum machine learning classiﬁers is proposed to predict the risk of heart *** proposed model is a bagging ensemble learning model where a quantum support vector classiﬁer was used as a base classiﬁ***-more,in order to make the model’s outcomes more explainable,the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations(SHAP)*** the experimental study,other stand-alone quantum classiﬁers,namely,quantum Support Vector Classiﬁer(QSVC),quantum Neural Network(QNN),and Variational quantum Classiﬁer(VQC)are applied and compared with classical machine learning classiﬁers such as Sup-port Vector machine(SVM),and Artiﬁcial Neural Network(ANN).The experi-mental results on the Cleveland dataset reveal the superiority of QSVC compared to the others,which explains its use in the proposed bagging *** Bagging-QSVC model outperforms all aforementioned classiﬁers with an accuracy of 90.16%while showing great competitiveness compared to some state-of-the-art models using the same *** results of the study indicate that quantum machine learning classiﬁers perform better than classical machine learning classi-ﬁers in predicting heart *** addition,the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classiﬁers.

关键词： machine learning ensemble learning quantum machine learning explainable machine learning heart disease prediction

Pancreatic Cancer Data Classification with quantum machine learning

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Journal of quantum Computing 2023年第1期5卷 1-13页

作者： Amit Saxena Smita Saxena Centre for Development of Advanced Computing(C-DAC) Pune411008India Bioinformatics Centre Savitribai Phule Pune UniversityPune411007India

quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics *** 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 *** 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 *** provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification *** a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution *** a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker *** Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing *** classical approach as well as the quantum-based techniques reported similar *** uniformity suggests that these methods effectively captured similar underlying patterns in the ***,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification *** affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.

关键词： quantum computing quantum machine learning quantum support vector machine multiclass classification

Variational data encoding and correlations in quantum-enhanced machine learning

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Chinese Physics B 2024年第9期33卷 298-306页

作者： Ming-Hao Wang Hua L¨u School of Physics Hubei UniversityWuhan 430062China School of Science Hubei University of TechnologyWuhan 430068China

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical *** paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum *** encoding strategies can significantly influence quantum computer ***,we address the need to counteract the inevitable noise that can hinder quantum *** primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm *** adapting the learning concept from machine learning,we render data encoding a learnable *** allowed us to study the role of quantum correlation in data *** numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional ***,we delve into the role of quantum correlation in enhancing task performance,especially in noisy *** findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

关键词： quantum machine learning variational data encoding quantum correlation

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Prediction of Solar Irradiation Using quantum Support Vector machine learning Algorithm

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Smart Grid and Renewable Energy 2016年第12期7卷 293-301页

作者： Makhamisa Senekane Benedict Molibeli Taele Faculty of Computing Botho University-Maseru Campus Maseru Lesotho Department of Physics and Electronics National University of Lesotho Roma Lesotho

Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.

关键词： quantum quantum machine learning machine learning Support Vector machine quantum Support Vector machine Energy Solar Irradiation

Recent advances for quantum classifiers

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Science China(Physics,Mechanics & Astronomy) 2022年第2期65卷 1-23页

作者： Weikang Li Dong-Ling Deng Center for quantum Information Institute for Interdisciplinary Information ScienceTsinghua UniversityBeijing 100084China Shanghai Qi Zhi Institute Shanghai 200232China

machine learning has achieved dramatic success in a broad spectrum of *** interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications,giving rise to an emergent research frontier of quantum machine *** this line,quantum classifiers,which are quantum devices that aim to solve classification problems in machine learning,have attracted tremendous attention *** this review,we give a relatively comprehensive overview for the studies of quantum classifiers,with a focus on recent ***,we will review a number of quantum classification algorithms,including quantum support vector machines,quantum kernel methods,quantum decision tree classifiers,quantum nearest neighbor algorithms,and quantum annealing based ***,we move on to introduce the variational quantum classifiers,which are essentially variational quantum circuits for *** will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem,where the training of quantum classifiers might be hindered by the exponentially vanishing *** addition,the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

关键词： quantum machine learning quantum classifiers quantum kernel methods variational quantum algorithms

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Universal adversarial examples and perturbations for quantum classifiers

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National Science Review 2022年第6期9卷 48-55页

作者： Weiyuan Gong Dong-Ling Deng Center for quantum Information Institute for Interdisciplinary Information Sciences (ⅢS) Tsinghua University Shanghai Qi Zhi Institute

quantum machine learning explores the interplay between machine learning and quantum physics, which may lead to unprecedented perspectives for both fields. In fact, recent works have shown strong evidence that quantum computers could outperform classical computers in solving certain notable machine learning tasks. Yet, quantum learning systems may also suffer from the vulnerability problem: adding a tiny carefully crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we study the universality of adversarial examples and perturbations for quantum classifiers. Through concrete examples involving classifications of real-life images and quantum phases of matter, we show that there exist universal adversarial examples that can fool a set of different quantum classifiers. We prove that, for a set of k classifiers with each receiving input data of n qubits, an O(ln [k]/2~n) increase of the perturbation strength is enough to ensure a moderate universal adversarial risk. In addition, for a given quantum classifier, we show that there exist universal adversarial perturbations, which can be added to different legitimate samples to make them adversarial examples for the classifier. Our results reveal the universality perspective of adversarial attacks for quantum machine learning systems, which would be crucial for practical applications of both near-term and future quantum technologies in solving machine learning problems.

关键词： quantum machine learning quantum classifiers adversarial examples measure concentration quantum no-free-lunch theorem

Practical Meta-Reinforcement learning of Evolutionary Strategy with quantum Neural Networks for Stock Trading

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Journal of quantum Information Science 2020年第3期10卷 43-71页

作者： Erik Sorensen Wei Hu Department of Computer Science Houghton College Houghton NY USA

We show the practicality of two existing meta-learning algorithms Model- Agnostic Meta-learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propo... 详细信息

We show the practicality of two existing meta-learning algorithms Model- Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our quantum meta-learning models on a simulated quantum computer with portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs have a similar learning pattern to our much larger classical meta-learned models that have over 250,000 model parameters and 2500 training epochs. Given these findings, we also discuss the benefits of scaling up our ex

关键词： Reinforcement learning Deep learning Meta-learning Evolutionary Strategy quantum Computing quantum machine learning Stock Market Algorithmic Trading

quantum partial least squares regression algorithm for multiple correlation problem

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Chinese Physics B 2022年第3期31卷 177-186页

作者： Yan-Yan Hou Jian Li Xiu-Bo Chen Yuan Tian School of Artificial Intelligence Beijing University of Post and TelecommunicationsBeijing 100876China College of Information Science and Engineering Zaozhuang UniversityZaozhuang 277160China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing 100876China GuiZhou University Guizhou Provincial Key Laboratory of Public Big DataGuiyang 550025China

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

关键词： quantum machine learning partial least squares regression eigenvalue decomposition

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