Quantum machine learning (QML) investigates whether quantum-mechanical representations and computations can improve machine-learning tasks. However, it is often unclear which observed performance differences are genuinely attributable to specifically quantum features rather than to architectural differences that can be implemented classically or benchmarking effects. This thesis studies the role of entanglement in variational QML models through a controlled ablation or removal framework. The work introduces partially and fully separable variants of the Circuit-centric Classifier (CCC) and IQP-Kernel Classifier, constructed by systematically reducing entangling interactions while keeping the remaining training and evaluation pipeline fixed. The models are evaluated on multiple synthetic and structured benchmark families, including but not limited to linearly separable, hidden-manifold, and hyperplane-based datasets. The results show that partially entangling variants often remain competitive with the original models, whereas fully separable variants typically exhibit larger performance degradation. The experiments are consistent with entanglement contributing meaningfully to performance in the studied models, while also suggesting that sparse entangling connectivity may already retain much of the observed benefit of the original architectures in this benchmark setting. The IQP-kernel experiments scaled prohibitively in higher-dimensional settings, exceeding the available computational budget.
Quantum machine learning (QML) investigates whether quantum-mechanical representations and computations can improve machine-learning tasks. However, it is often unclear which observed performance differences are genuinely attributable to specifically quantum features rather than to architectural differences that can be implemented classically or benchmarking effects. This thesis studies the role of entanglement in variational QML models through a controlled ablation or removal framework. The work introduces partially and fully separable variants of the Circuit-centric Classifier (CCC) and IQP-Kernel Classifier, constructed by systematically reducing entangling interactions while keeping the remaining training and evaluation pipeline fixed. The models are evaluated on multiple synthetic and structured benchmark families, including but not limited to linearly separable, hidden-manifold, and hyperplane-based datasets. The results show that partially entangling variants often remain competitive with the original models, whereas fully separable variants typically exhibit larger performance degradation. The experiments are consistent with entanglement contributing meaningfully to performance in the studied models, while also suggesting that sparse entangling connectivity may already retain much of the observed benefit of the original architectures in this benchmark setting. The IQP-kernel experiments scaled prohibitively in higher-dimensional settings, exceeding the available computational budget.
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 Machine Learning (QML) has emerged as a promising paradigm that combines principles of quantum computation with classical learning techniques to address challenges posed by complex, high-dimensional, and non-linear datasets. This work presents a systematic experimental study of quantum-enhanced classification and clustering using hybrid quantum–classical models evaluated on synthetically generated datasets. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) are implemented using the Qiskit framework and benchmarked against established classical algorithms. The synthetic datasets are carefully designed to control linearity, dimensionality, class overlap, and nonlinear structure, enabling a detailed performance comparison under both ideal and noisy simulation conditions. The experimental results indicate that quantum feature representations can enhance class separability in non-linear scenarios, while also highlighting the practical limitations imposed by noise sensitivity and circuit depth constraints inherent to Noisy Intermediate-Scale Quantum (NISQ) devices .
Quantum machine learning (QML) promises new representational and computational capabilities, yet practical deployment on near-term hardware is hampered by resource overheads, depth constraints, and fragile trainability. This thesis advances resource-aware QML by proposing architectures and kernels that retain expressivity while sharply reducing qubit counts, circuit depth, and entangling-gate budgets. The work is presented in an article-based format with three core contributions. First, I introduce a Coherent Feed-Forward Quantum Neural Network (CFF-QNN) that preserves quantum coherence across all layers, mirrors the flexibility of classical feed-forward networks (adjustable hidden layers and nodes), and decouples qubit requirements from input feature dimension. Compared to prevailing QNN baselines, the CFF-QNN reduces both depth and CNOT count by over 50% while achieving strong performance on standard benchmarks (e.g., 91% accuracy on Wisconsin breast cancer and 85% on credit-card fraud). This contribution is accompanied by an international patent filing (WO2025050205A1). Second, I develop a resource-efficient quantum kernel that enables high-dimensional embeddings with substantially fewer qubits and entanglers, achieving linear scaling of entangling gates in the number of qubits. Empirically, the kernel delivers competitive or superior performance to widely used classical kernels and to popular quantum feature maps (e.g., on the Parkinson's disease dataset), with noisy simulations and small-scale runs on superconducting hardware indicating suitability for near-term devices. This contribution is covered by a companion patent filing (WO2025073041A1). Third, I propose a quantum reservoir computing (QRC) scheme that reuses a fixed quantum feature-map circuit as the reservoir while injecting temporal memory via an explicit feedback loop. The register is recycled across time, so quantum resources remain constant, no quantum parameters are trained, and only a lightweight classical readout is fitted. Experiments on chaotic time series (e.g., Mackey-Glass) show competitive predictive accuracy with strict resource efficiency, and illuminate how feedback delay and entangling structure affect memory and error. Collectively, these results chart a path toward practical QML: coherent architectures and kernels that are trainable, scalable in input dimension, frugal in quantum resources, and viable on noisy intermediate-scale devices—while providing design guidelines for future, larger-scale implementations.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) devices remains an empirical question, especially because training depth and scale can introduce optimization challenges such as barren plateaus. Here we study how the number and topology of two-qubit entangling gates in the feature-map stage influence a fixed hybrid QNN workflow for classifying strong versus weak epitope-receptor binding in Porcine Reproductive and Respiratory Syndrome (PRRS) vaccine design. The dataset consists of docking-derived binding affinities for N=80 9-mer epitopes, labeled as Strong or Weak binding, and partitioned into training, validation, and test subsets using a 40:30:30 split. We compare a classical CNN benchmark with a hybrid Embedding-QNN architecture under four feature-map configurations: a non-entangling Z feature map, an all-to-all high-entanglement ZZ feature map, and two interleaved nearest-neighbour entanglement patterns of low and high depth. Among the configurations tested, the high-entanglement ZZ feature map is seen to provide the strongest evidence of reduced training-set overfit, with a lower training area under the accuracy curve (AUAC) and the highest test/training AUAC ratio, while preserving competitive test-set accuracy. These results do not establish a general QML advantage, but they suggest that feature-map entanglement topology is a meaningful design variable for sparse biological screening tasks and warrants further evaluation with additional metrics, larger datasets, and noise-aware or hardware-based experiments.
This thesis presents a comparative study of quantum machine learning and classical machine learning developed from first principles. It begins by establishing the foundations of the quantum circuit model, observables, and projective measurement, and then revisits the classical learning framework with an emphasis on risk minimisation, task-aligned metrics, and rigorous evaluation protocols. Building on this shared perspective, the thesis examines two central paradigms in quantum machine learning: quantum kernels and variational quantum circuits. A unifying theme is that quantum models must be assessed fairly against strong classical baselines and under realistic hardware constraints. The work therefore explicitly accounts for the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, restricted connectivity, compilation overhead, and finite-shot estimation. The empirical study instantiates both quantum pipelines on a controlled supervised benchmark adapted from IBM Quantum: classifying small synthetic images containing either a vertical or a horizontal line under bounded noise. Using the same dataset, identical splitting strategies, and matched budgets for shots and optimisation, the thesis compares quantum and classical models under a consistent evaluation protocol. The results show that both quantum kernel and variational approaches achieve meaningful accuracy in this regime, but do not demonstrate a decisive quantum advantage on the selected task. The thesis concludes by outlining limitations and directions for future work, including scaling to richer benchmarks, incorporating error mitigation, and strengthening assumption-explicit methodology for credible claims of quantum-enhanced learning.