Abstract Quantum machine learning is one of most exciting prospective applications of quantum technologies. Quantum kernel SVM is one of most popular methods for classifiers. However, there are few reports that show the quantum advantage in learning model construction compared with classical learning models. In this work, we investigated the process of learning model construction of quantum machine learning by using an evaluation index and ROC space graph. The behaviour between quantum and classical machine learning is different, and the quantum learning model is the method to decrease the false positive rate from high false and true positive rates that is based on quantum superposition. In summary, we have demonstrated the effect of quantum superposition on quantum machine learning for the first time from actual experimental results. This truth supports that quantum machine learning can build an outline of learning models faster than classical methods even if the number of original data is smaller.
Abstract Quantum machine learning is one of most exciting prospective applications of quantum technologies. Quantum kernel SVM is one of most popular methods for classifiers. However, there are few reports that show the quantum advantage in learning model construction compared with classical learning models. In this work, we investigated the process of learning model construction of quantum machine learning by using an evaluation index and ROC space graph. The behaviour between quantum and classical machine learning is different, and the quantum learning model is the method to decrease the false positive rate from high false and true positive rates that is based on quantum superposition. In summary, we have demonstrated the effect of quantum superposition on quantum machine learning for the first time from actual experimental results. This truth supports that quantum machine learning can build an outline of learning models faster than classical methods even if the number of original data is smaller.
Abstract For many manufacturing companies, the production line is very important. In recent years, the number of small-quantity, high-mix products have been increasing, and the identification of good and defective products must be carried out efficiently. At that time, machine learning is a very important issue on shipping inspection using small amounts of data. Quantum machine learning is one of most exciting prospective applications of quantum technologies. SVM using kernel estimation is one of most popular methods for classifiers. Our purpose is to search quantum advantage on classifier to enable us to classifier in inspection test for small size datasets. In this study, we made clear the difference between classical and quantum kernel learning in initial state and propose analysis of learning process by plotting ROC space. To meet the purpose, we investigated the effect of each feature map compared to classical one, using evaluation index. The simulation results show that the learning model construction process between quantum and classical kernel learning is different in initial state. Moreover, the result indicates that the learning model of quantum kernel is the method to decrease the false positive rate (FPR) from high FPR, keeping high true positive rates on several datasets. We demonstrate that learning process on quantum kernel is different from classical one in initial state and plotting to ROC space graph is effective when we analyse the learning model process.
Outgoing inspection of the production line is very important for many manufacturing companies. In recent years, two class classification of good and defective products must be carried out efficiently as the number of small-quantity, high-mix products have been increasing. The support vector machine with kernel method (kernel learning) is the most popular method in two class classification used in factories. Quantum kernel learning is one of the most promising applications of quantum technology. In this study, we propose an analysis method that allows us to know the difference between classical and quantum machine learning by plotting false positive rate (FPR) and true positive rate (TPR) on Receiver Operating Characteristic (ROC) space. Quantum machine learning uses quantum data with feature maps. We compared quantum and classical data using a small-sized existing datasets. First, it was not possible to confirm that the quantum data incorporating quantum entanglement could achieve the effect in this study. Next, when we examined the learning process of quantum data with Pauli feature map and classical data, we observed differences in the initial learning process. Based on these results, in contrast to the commonly used ROC curve, we plotted the FPR and TPR separately on the ROC space according to the training size. Plotting on ROC space in this experiment shows that quantum kernel learning is a method to reduce only FPR from high FPR and TPR. We found that the kernel learning process using quantum data is different from one using classical data.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
Quantum Machine Learning (QML) has emerged as a promising paradigm for addressing increasing computational and representational demands in materials informatics. While classical models such as Support Vector Machines (SVM) achieve strong predictive performance, they often struggle to capture complex, highly correlated interactions in high-dimensional materials data. QML addresses this challenge by leveraging quantum-mechanical principles to construct expressive feature embeddings, where prospective quantum advantage lies in generating feature spaces that are difficult to approximate classically. In this study, 1,000 crystalline compounds from the Open Quantum Materials Database (OQMD) are evaluated in a binary classification task based on formation-energy stability. The dataset is normalized, reduced to four dimensions via Principal Component Analysis (PCA), and encoded into quantum circuits. Three QML models—QSVM, VQC, and QNN—are benchmarked against a classical SVM using repeated stratified evaluation. Results show that the classical SVM achieves the highest accuracy (91.8% ± 0.012), followed by QSVM (60.8% ± 0.035), while VQC and QNN perform significantly worse. This gap is driven by limited qubit capacity, encoding inefficiencies, restricted circuit expressivity, and optimization challenges. Nevertheless, QSVM demonstrates stable performance, suggesting that potential quantum advantage may emerge from improved feature encoding and kernel design rather than deeper variational circuits.
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.
Quantum computing holds the promise of revolutionizing various fields, including machine learning, by leveraging quantum phenomena to perform computations beyond the capabilities of classical computers. In this paper, we present a comparative study of quantum and classical algorithms in the context of machine learning tasks. We examine the advantages of quantum algorithms, such as quantum parallelism and entanglement, and their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization. Through theoretical analysis and empirical experiments, we demonstrate the strengths and limitations of quantum machine learning algorithms, highlighting their potential for achieving quantum advantage in certain scenarios. Additionally, we discuss the challenges and opportunities for integrating quantum computing into machine learning pipelines and outline future research directions in this rapidly evolving field.