This study presents a performance analysis of Quantum Neural Networks (QNN) versus classical benchmarks. We compare three architectures: Classical Artificial Neural Networks (ANN), Variational QNNs (VQNN), and Post-Variational QNNs (Post-QNN) across standard classification datasets (PIMA Diabetes, Heart Disease, and Iris). While VQNNs are theoretically powerful, our experiments reveal significant challenges in hyperparameter tuning and stability, often resulting in suboptimal accuracy. Conversely, the Post-QNN approach—which leverages fixed quantum feature maps and classical optimization—demonstrates superior stability. Our results show that Post-QNNs not only outperform VQNNs by wide margins (e.g., +20% accuracy on Heart Disease) but also match or exceed classical ANN performance. This research highlights the efficacy of Post-Variational methods in overcoming the trainability barriers of standard parameterized quantum circuits.
This study presents a performance analysis of Quantum Neural Networks (QNN) versus classical benchmarks. We compare three architectures: Classical Artificial Neural Networks (ANN), Variational QNNs (VQNN), and Post-Variational QNNs (Post-QNN) across standard classification datasets (PIMA Diabetes, Heart Disease, and Iris). While VQNNs are theoretically powerful, our experiments reveal significant challenges in hyperparameter tuning and stability, often resulting in suboptimal accuracy. Conversely, the Post-QNN approach—which leverages fixed quantum feature maps and classical optimization—demonstrates superior stability. Our results show that Post-QNNs not only outperform VQNNs by wide margins (e.g., +20% accuracy on Heart Disease) but also match or exceed classical ANN performance. This research highlights the efficacy of Post-Variational methods in overcoming the trainability barriers of standard parameterized quantum circuits.
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies
In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of hybrid quantum-classical neural networks. These networks combine the power of classical neural networks with the computational advantages offered by quantum variational circuits. This study presents a comprehensive survey of hybrid quantum-classical machine learning algorithms, focusing on their architecture, performance, and hyperparameter optimization techniques. Introducing the concept of hybrid quantum-classical machine learning and its significance in contemporary image classification tasks. It explores the theoretical foundations of quantum variational circuits and their integration with classical neural network architectures. Special attention is given to prominent algorithms such as Quantum Convolutional Neural Networks, Quanvolutional Neural Networks, and Quantum ResNet, each offering unique advantages in image classification tasks. A major aspect covered in this study is the hyperparameter optimization techniques employed in hybrid quantum-classical machine learning. The chapter discusses quantum-inspired optimization methods tailored for efficiently tuning the hyperparameters of complex neural network models. Through comparative analysis, the effectiveness of these optimization techniques is demonstrated in reducing computational overhead and improving model performance. Moreover, the study also delves into experimental results obtained from benchmarking hybrid quantum-classical models against traditional classical approaches. It highlights the accuracy variations observed across different quantum architectures and explores the impact of architectural permutations on model convergence. Insights gained from these experiments provide valuable guidance for researchers and practitioners seeking to leverage hybrid quantum-classical neural networks in real-world applications.
Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum Circuits (VQCs) at their core and featuring layers of quantum gates optimized by classical algorithms, have garnered significant attention. However, a rigorous and exhaustive evaluation of their practical performance remains largely incomplete. In this study, we conduct a comprehensive comparative analysis of three prominent hybrid classical-quantum architectures: Quantum Convolutional Neural Networks (QCNN), Quantum Recurrent Neural Networks (QRNN), and Quantum Vision Transformers (QViT), focusing on the critical dimensions of generalization, accuracy, and robustness. Our findings provide novel insights that address previous evaluative gaps. Notably, while these models exhibit exceptional performance on low-feature datasets such as MNIST, their learning efficacy degrades significantly when transitioned to high-feature datasets. Furthermore, convolutional-based models like QCNN appear less effective on high-dimensional data than other machine learning architectures. Additionally, while all models are susceptible to adversarial noise, traditional architectures, such as recurrent and convolutional networks, demonstrate superior resilience. Conversely, in the presence of quantum noise, the transformer-based architecture proves its strength by maintaining high robustness against measurement noise, channel noise, and finite-shot effects, whereas other architectures suffer marked performance declines. These results provide a granular perspective on the current state of the field and underscore the critical importance of tailoring model selection to the constraints of contemporary Noisy Intermediate-Scale Quantum (NISQ) environments.
This study presents a comparative evaluation of Artificial Neural Networks (ANNs) and Variational Quantum Circuits (VQCs) for biomedical classification, using the Wisconsin Breast Cancer Diagnostic dataset. Both models were optimized using Bayesian hyperparameter tuning via Optuna to ensure a fair performance comparison. The ANN achieved high predictive accuracy (98.2%), F1 score (98.2%), and Area Under the Curve (AUC) (0.98), exhibiting stable convergence and efficient training. The VQC, though trained under classical simulation, attained a respectable accuracy of 86.5% and an AUC of 0.85, with notably strong recall (99.1%) for malignant cases, highlighting its potential in scenarios requiring high sensitivity. Loss curves, confusion matrices, and hyperparameter importance visualizations were used to interpret each model’s training behaviour and decision boundaries. While classical models remain superior in current biomedical classification tasks, VQCs offer promising computational advantages and potential scalability for complex, high-dimensional datasets. This work provides early benchmarks for quantum-classical comparisons in biomedical machine learning and offers guidance for future implementations as quantum hardware becomes more accessible.
Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.
Quantum computers promise improving machine learning. We investigated the performance of new quantum neural network designs. Quantum neural networks currently employed rely on a feature map to encode the input into a quantum state. This state is then evolved via a parameterized variational circuit. Finally, a measurement is performed and post-processed on a classical computer to extract the prediction of the quantum model. We develop a new technique, where we merge feature map and variational circuit into a single parameterized circuit and post-process the results using a classical neural network. On a variety of real and generated datasets, we show that the new, combined approach outperforms the separated feature map & variational circuit method. We achieve lower loss, better accuracy, and faster convergence.