Machine learning and quantum computing are two technologies that are causing a paradigm shift in the performance and behavior of certain algorithms, achieving previously unattainable results. Machine learning (kernel classification) has become ubiquitous as the forefront method for pattern recognition and has been shown to have numerous societal applications. While not yet fault-tolerant, Quantum computing is an entirely new method of computation due to its exploitation of quantum phenomena such as superposition and entanglement. While current machine learning classifiers like the Support Vector Machine are seeing gradual improvements in performance, there are still severe limitations on the efficiency and scalability of such algorithms due to a limited feature space which makes the kernel functions computationally expensive to estimate. By integrating quantum circuits into traditional ML, we may solve this problem through the use of quantum feature space, a technique that improves existing Machine Learning algorithms through the use of parallelization and the reduction of the storage space from exponential to linear. This research expands on this concept of the Hilbert space and applies it for classical machine learning by implementing the quantum-enhanced version of the K nearest neighbors algorithm. This paper first understands the mathematical intuition for the implementation of quantum feature space and successfully simulates quantum properties and algorithms like Fidelity and Grover's Algorithm via the Qiskit python library and the IBM Quantum Experience platform. The primary experiment of this research is to build a noisy variational quantum circuit KNN (QKNN) which mimics the classification methods of a traditional KNN classifier. The QKNN utilizes the distance metric of Hamming Distance and is able to outperform the existing KNN on a 10-dimensional Breast Cancer dataset.
Machine learning and quantum computing are two technologies that are causing a paradigm shift in the performance and behavior of certain algorithms, achieving previously unattainable results. Machine learning (kernel classification) has become ubiquitous as the forefront method for pattern recognition and has been shown to have numerous societal applications. While not yet fault-tolerant, Quantum computing is an entirely new method of computation due to its exploitation of quantum phenomena such as superposition and entanglement. While current machine learning classifiers like the Support Vector Machine are seeing gradual improvements in performance, there are still severe limitations on the efficiency and scalability of such algorithms due to a limited feature space which makes the kernel functions computationally expensive to estimate. By integrating quantum circuits into traditional ML, we may solve this problem through the use of quantum feature space, a technique that improves existing Machine Learning algorithms through the use of parallelization and the reduction of the storage space from exponential to linear. This research expands on this concept of the Hilbert space and applies it for classical machine learning by implementing the quantum-enhanced version of the K nearest neighbors algorithm. This paper first understands the mathematical intuition for the implementation of quantum feature space and successfully simulates quantum properties and algorithms like Fidelity and Grover's Algorithm via the Qiskit python library and the IBM Quantum Experience platform. The primary experiment of this research is to build a noisy variational quantum circuit KNN (QKNN) which mimics the classification methods of a traditional KNN classifier. The QKNN utilizes the distance metric of Hamming Distance and is able to outperform the existing KNN on a 10-dimensional Breast Cancer dataset.
Abstract This study presents a comparison between classical machine learning (ML) algorithms and their quantum-enhanced counterparts in classifying scikit’s breast cancer dataset. Specifically, the research focused on evaluating the accuracy and time computational efficiency of quantum-enhanced machine learning (QEML) algorithms against traditional or classical machine learning algorithms. The quantum algorithms employed include the quantum support vector machine (qSVM), variational quantum classifier (VQC) and quantum k-nearest neighbor (qkNN). The qSVM and qkNN algorithms discussed here are really kernel-based hybrid classical-quantum algorithms as opposed to fully quantum algorithms and the VQC is a hybrid classical-quantum algorithm. These algorithms were tested against their classical equivalents: support vector machine (SVM), k-nearest neighbor (kNN), and artificial neural network (ANN). The qSVM algorithm demonstrated an 90% accuracy in distinguishing malignant from benign cancer cases (the classical support vector machine algorithm had a 99% accuracy), while qkNN achieved 90% accuracy (the classical kNN algorithm had an accuracy of 97%), and VQC reported 88.77± 0.26% (averaged over 5 runs) (this was compared with an ANN which had an accuracy of 96%). The accuracies of the classical SVM, kNN, and ANN algorithms were recorded, to provide a basis for comparison. The results indicated that while QEML algorithms perform reasonably well in classification tasks, however they do not consistently outperform classical algorithms. Additionally, the study measured the time efficiency of each algorithm. The ANN required 33.8 seconds, kNN required 0.21 seconds, and SVM required 1.98 seconds to perform the classification tasks. In contrast, the quantum algorithms were significantly slower, with the VQC taking 632.53 seconds, qkNN requiring 495.34 seconds, and qSVM requiring 538.05 seconds (This is not fixed per run but give an impression of the time scales to run the algorithms). We emphasize that this simulation runtime does not reflect actual quantum hardware performance, where gate execution and circuit depth constraints differ significantly. To mitigate this ambiguity, we distinguish between simulation runtime and estimated quantum runtime where relevant. This work potentially highlights the cases where it may not offer any advantage to use quantum machine learning algorithms. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) analysis are also considered in this work where the values measured for the SVM, kNN and ANN were 99%, 99% and 99% respectively and for the qSVM, qkNN and VQC were 87%, 86% and 86± 0.2% (averaged over 5 runs) respectively. Overall, this research contributes valuable insights into the comparative performance of classical and quantum ML algorithms in a specific application, underscoring the potential and current limitations of quantum approaches in machine learning tasks. Hyperparameter optimization was done for all the algorithms, both quantum and classical, with Optuna. This was primarily because the QEML algorithms had more parameters and required considerable computational resources to optimize. This could have influenced the performance outcomes and is the basis for future work. The development of key comparison criteria is critical in the development of quantum machine learning, and this work offers some building blocks for this.
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 has emerged as a promising paradigm that leverages quantum computational principles to potentially outperform classical approaches on specific learning tasks. This paper presents a comprehensive empirical investigation comparing Quantum Machine Learning (QML), specifically Quantum Support Vector Machines (QSVM) against Classical Machine Learning (CML) baselines including Logistic Regression, K Nearest Neighbors and classical SVMs. We implement quantum kernel methods using variational quantum circuits with angle embedding, IQP and ZZ feature maps on datasets exhibiting varying structural complexity. Our experiments on synthetic quantum generated datasets (Quantum Parity, Quantum Entanglement, Quantum Interference) demonstrate that QSVM achieves superior classification accuracy with improvements ranging from 2.9 % to 60 % over the best classical baselines achieving a 55.6 % overall win rate. Notably, on the Quantum Parity dataset, QSVM consistently outperformed all classical models across multiple configurations. On real-world benchmark datasets (Breast Cancer, Iris, Wine), QSVM Angle achieved 88% accuracy on Breast Cancer compared to 76% for classical SVM Linear a 15.8 % relative improvement while matching the best classical performance on Iris(100%)and Wine(92%). All experiments were conducted using quantum circuit simulation on classical hardware. These results provide empirical evidence that quantum kernel methods offer meaningful advantages over classical approaches on datasets with complex feature interactions. We discuss the theoretical foundations of these improvements including quantum feature space expressivity and kernel alignment.
Quantum assisted machine learning (QML) have the potential to outperform the classical Machine Learning models by utilizing the quantum feature maps embeddings and variational circuits. In this study, a Hybrid Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) is proposed for supervised classification applied to the Breast Cancer Wisconsin Dataset. The QSVM component employs fidelity quantum kernels with three distinct feature maps—Z, ZZ and Pauli—to transform classical data into quantum states. The QNN is implemented using a variational quantum classifier based on the EstimatorQNN, which enables quantum-trainable parameters for enhanced feature extraction. The proposed hybrid methodology is evaluated using seven principal features, and experiments are conducted on IBM’s state vector Qiskit Aer local simulator. Performance is assessed based on accuracy, recall, precision, F1-score, and confusion matrix. Additionally, statistical significance is evaluated using paired t test and F test to compare the performance of different quantum feature maps and to validate improvements made in the Hybrid Model. The results demonstrate that the hybrid QSVM/QNN approach improves classification performance not only in terms of accuracy by achieving above 90% accuracy but also by reducing computational cost. This work contributes to the advancement of quantum enhanced machine learning (ML) algorithms by demonstrating the hybrid quantum-classical approach in structured data classification.
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-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.