The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationally expensive to estimate. Quantum-enhanced Support Vector Machine (QSVM) utilizes the properties of quantum computers to obtain an exponential speed up with respect to the conventional SVM by using the quantum state space as the feature space. Unfortunately, to benefit from these advantages, currently, it is necessary to have a background knowledge about quantum computing concepts and specific language skills. The goal of this paper is to introduce a web-based tool to make simple to all researchers, coming from several and heterogeneous scientific backgrounds, the application of QSVM to different real-world classification problems. To achieve this goal, the presented web tool involves data preprocessing techniques and a user-friendly interface. The suitability of the presented web tool is shown in an experimental session where QSVM is applied to solve well-known classification tasks.
The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationally expensive to estimate. Quantum-enhanced Support Vector Machine (QSVM) utilizes the properties of quantum computers to obtain an exponential speed up with respect to the conventional SVM by using the quantum state space as the feature space. Unfortunately, to benefit from these advantages, currently, it is necessary to have a background knowledge about quantum computing concepts and specific language skills. The goal of this paper is to introduce a web-based tool to make simple to all researchers, coming from several and heterogeneous scientific backgrounds, the application of QSVM to different real-world classification problems. To achieve this goal, the presented web tool involves data preprocessing techniques and a user-friendly interface. The suitability of the presented web tool is shown in an experimental session where QSVM is applied to solve well-known classification tasks.
The quantum support vector machines (QSVMs) offer a paradigm change that may leverage quantum mechanical processes to outperform traditional support vector machines (SVMs) in high-dimensional and nonlinear classification tasks. These machines are located at the crossroads of supervised machine learning and quantum computing. The purpose of this chapter is to provide a comprehensive technical analysis of QSVMs, with a particular emphasis on the use of variation quantum circuits for implicit feature space transformations and the development of these circuits within the context of quantum kernel estimation. Through the use of quantum state preparation and interference, we provide a comprehensive explanation of the possible fabrication of quantum kernels that are based on inner product evaluations in exponentially huge Hilbert spaces. When it comes to boosting model expressivity beyond classical limits, the importance that entanglement and quantum feature mapping play is specifically stressed. In addition to this, we investigate algorithmic implementations on noisy intermediate-scale quantum (NISQ) hardware, focusing on problems associated with quantum decoherence, gate fidelity, and loop depth optimization. Several fields, including quantum-enhanced drug discovery, anomaly detection in quantum sensor networks, fault classification in quantum error correction, and portfolio optimization, are now the focus of research that is being conducted in order to develop real-world applications. This chapter comes to a close with an analysis of the existing theoretical limits, resource needs, and future perspectives for the use of QSVMs in machine learning applications in order to achieve a quantum advantage. In order to facilitate the development of scalable quantum machine learning models that are founded on strong mathematical and computational notions, this synthesis is geared at academics and practitioners.
With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.
Quantum Support Vector Machines (QSVMs) blend the interesting worlds of quantum computing and machine learning, which offers fresh approaches for tackling complex classification problems in high-dimensional and non-linear data spaces. This chapter provides a comprehensive exploration of QSVMs, beginning with the fundamentals of quantum computing concepts such as qubits, superposition, and entanglement, which enable quantum algorithms to surpass classical computational limits. Next, we look at how QSVMs are mathematically expressed, highlighting the use of kernel techniques and quantum feature maps to embed classical data into exponentially huge Hilbert spaces.The implementation of QSVMs using frameworks like Qiskit, PennyLane, and TensorFlow Quantum is discussed in detail, accompanied by code examples and case studies, including binary classification on the two-moons dataset. Applications of QSVMs across domains such as healthcare, finance, cybersecurity, natural language processing, materials science, and climate sustainability are highlighted, demonstrating their broad relevance. The chapter also addresses challenges related to noise, scalability, and hybrid integration in the NISQ era, and explores future directions involving error correction, deep learning integration, and cloud-based deployment. Together, these insights illustrate QSVMs’ potential as a transformative tool in next-generation machine learning.
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 quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks. Through a comparative analysis, we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality. As a case study, we explored the potential of quantum paradigms in the context of a real-world problem: classifying pancreatic cancer biomarker data. The 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 framework. The classical approach as well as the quantum-based techniques reported similar accuracy. This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset. Remarkably, quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency. This 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.
The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.
Classification is a fundamental task in machine learning that aims to categorize input samples into predefined classes based on their features. It has widespread applications in areas such as spam detection and image classification. Support Vector Machines (SVM) are a widely-used supervised learning method that seeks to maximize the margin between decision boundaries, particularly excelling in non-linear classification tasks through kernel functions. In this study, the SVM classification problem is reformulated as an optimization problem and represented using Quadratic Unconstrained Binary Optimization (QUBO) to leverage quantum computing for solving. To enhance the solution process, the simulated annealing algorithm is employed, improving convergence speed and boosting both the efficiency and performance of the classification task.