In this paper, we present classical machine learning algorithms enhanced by quantum technology to classify a data set. The data set contains binary input variables and binary output variables. The goal is to extend classical approaches such as neural networks by using quantum machine learning principles. Classical algorithms struggle as the dimensionality of the feature space increases. We examine the usage of quantum technologies to speed up these classical algorithms and to introduce the new quantum paradigm into machine diagnostic domain. Most of the prognosis models based on binary or multi-valued classification have become increasingly complex throughout the recent past. Starting with a short introduction into quantum computing, we will present an approach of combining quantum computing and classical machine learning to the reader. Further, we show different implementations of quantum classification algorithms. Finally, the algorithms presented are applied to a data set. The results of the implementations are shown and discussed in the last section.
In this paper, we present classical machine learning algorithms enhanced by quantum technology to classify a data set. The data set contains binary input variables and binary output variables. The goal is to extend classical approaches such as neural networks by using quantum machine learning principles. Classical algorithms struggle as the dimensionality of the feature space increases. We examine the usage of quantum technologies to speed up these classical algorithms and to introduce the new quantum paradigm into machine diagnostic domain. Most of the prognosis models based on binary or multi-valued classification have become increasingly complex throughout the recent past. Starting with a short introduction into quantum computing, we will present an approach of combining quantum computing and classical machine learning to the reader. Further, we show different implementations of quantum classification algorithms. Finally, the algorithms presented are applied to a data set. The results of the implementations are shown and discussed in the last section.
Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges.
In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
Machine Learning (ML) has changed the way computers work by letting them learn from data and make smart choices. However, traditional ML techniques encounter scalability and efficiency constraints as datasets get larger and computational problems become more complicated[1]. These restrictions could be circumvented by the new paradigms that quantum computing offers by using entanglement and superposition [2], [3]. The combination of the Quantum computing with the Machine Learning is called the Quantum Machine Learning(QML), offers efficiency improvements, new algorithmic opportunities, and speedups in certain linear algebraic operations. So, this review dives deeply into quantum-classical algorithms, with a particular emphasis on the approaches like quantum neural network and quantum support vector machines. We examine computational complexity, architectural design, benchmarking findings, and real-world applications (such as optimization, cryptography, and classification). We also discuss important issues including scalability, data encoding, and noise, and we suggest future paths for error prevention and scalable structures.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
In recent years, Machine Learning (ML) has started to be ubiquitously applied to practically most of the human activity domains. Although traditional, or classical machine learning (CML) approaches are useful in solving many complex tasks, there are still many challenges that such approaches are facing. One issue is the limitation in the processing speed of current silicon technology based computers, which made researchers look to the underlying quantum theory principles and try to run complex quantum computing experiments. In future, in order to develop more complex artificial intelligence systems, we will have to process huge amounts of data at high speed, and the existing classical computing will probably not serve well this purpose, due to silicon technology limitations. A different technology that can handle huge volumes of data and at high speed is needed. In recent years, the progress in quantum computing research seems to provide hopeful answers to overcome the speed processing barrier. This is very important for the training of many computational intensive machine learning models. The latest advancements in quantum technology appear to be promising, which can boost the field of machine learning overall. In this chapter, we discuss the transition from classical machine learning to quantum machine learning (QML) and explore the recent progress in this domain. QML is not only associated with the development of high-performance machine learning algorithms that can run on a quantum computer with significant performance improvements but also has a very diverse meaning in other aspects. The chapter tried to touch those aspects in brief too, but the main focus is on the advancements in the field of developing machine learning algorithms that will run on a quantum computer.