With the growing interest in hybrid quantumclassical intelligence, Quantum Machine Learning (QML) offers a new frontier for learning complex, highdimensional data like EEG signals. Most current QML methodologies, however, are plagued by instability against noisy quantum states, low fidelity, and poor feature encoding mechanisms. To overcome these drawbacks, this study introduces a new framework named “Enhancing Quantum Machine Learning Models with Error-Tolerant Superposition-Based Feature Extraction and State Fidelity Boosting”. The approach combines angle-based quantum superposition embedding with fidelity-based quantum autoencoder for retaining the integrity of quantum states during training. Classification is performed using a Variational Quantum Circuit (VQC) with fidelity loss minimization included in training to eliminate decoherence effects. The model is deployed with Pennylane and Qiskit on Python, and tested on a publicly shared EEG dataset from Kaggle on emotion classification. The model has a classification accuracy of 93.5%, a 4.3% increase compared to the bestperforming current QML method. The fidelity score was also enhanced to 0.95, which ensures that robust state is well-preserved. The work unfolds tremendous potential to employ QML in actual bio-signal applications. The fidelity-aware filtering and superposition encoding pipeline of the proposed method can be trivially generalized to other high-noise, high-dimensional quantum learning problems.
With the growing interest in hybrid quantumclassical intelligence, Quantum Machine Learning (QML) offers a new frontier for learning complex, highdimensional data like EEG signals. Most current QML methodologies, however, are plagued by instability against noisy quantum states, low fidelity, and poor feature encoding mechanisms. To overcome these drawbacks, this study introduces a new framework named “Enhancing Quantum Machine Learning Models with Error-Tolerant Superposition-Based Feature Extraction and State Fidelity Boosting”. The approach combines angle-based quantum superposition embedding with fidelity-based quantum autoencoder for retaining the integrity of quantum states during training. Classification is performed using a Variational Quantum Circuit (VQC) with fidelity loss minimization included in training to eliminate decoherence effects. The model is deployed with Pennylane and Qiskit on Python, and tested on a publicly shared EEG dataset from Kaggle on emotion classification. The model has a classification accuracy of 93.5%, a 4.3% increase compared to the bestperforming current QML method. The fidelity score was also enhanced to 0.95, which ensures that robust state is well-preserved. The work unfolds tremendous potential to employ QML in actual bio-signal applications. The fidelity-aware filtering and superposition encoding pipeline of the proposed method can be trivially generalized to other high-noise, high-dimensional quantum learning problems.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.
Quantum Machine Learning (QML) offers a paradigm shift in computational modeling, potentially surpassing the limitations of classical algorithms. This paper investigates the application of QML techniques to enhance feature extraction and modeling, focusing on efficient data processing and improved model performance, particularly in scenarios involving high-dimensional data and complex datasets. We explore the core mechanism of leveraging quantum properties to accelerate feature extraction, refine model representations, and ultimately boost the effectiveness of machine learning models. The research details a novel approach to feature representation using quantum algorithms, demonstrating significant improvements across various datasets. This work presents a framework for intelligently extracting key features from data, enabling the development of more robust and accurate models. The core claim is that QML can significantly enhance machine learning performance, particularly with complex datasets and high dimensionality.
Quantum machine learning (QML) is an emerging research area focused on enhancing the diagnostic accuracy and interpretability of biomedical signals. In this study, we propose a Quantum Variational Autoencoder (QVAE) framework for EEG-ECG-based mental health diagnostics, integrating quantum amplitude encoding, entanglement-driven latent space transformation, and a hybrid quantum-classical attention mechanism. The proposed QVAE model leverages quantum computational techniques to enhance classification performance, signal reconstruction, and latent feature interpretability. We introduce a quantum Wasserstein-Fisher Hybrid Loss Function, which integrates Quantum Wasserstein Distance (QWD) and Quantum Fisher Information (QFI) for multimodal physiological data. Through extensive experimentation on EEG and ECG datasets from Brainwave and PhysioNet repositories, the proposed model achieved a classification accuracy of 97.8% and an error of 0.007, significantly outperforming baseline CNNs, classical variational autocoders, and classical variationals. Importantly, extensive simulations on IBM Qiskit and PennyLane quantum simulators demonstrate the feasibility of the proposed framework on quantum simulators, supporting its potential for deployment on near-term quantum hardware. This study contributes to quantum machine learning for healthcare by achieving high diagnostic accuracy and robust signal reconstruction, demonstrating its suitability for immediate clinical translation into quantum-enhanced EEG diagnostics.
Quantum Machine Learning (QML) represents a paradigm shift in computational neuroscience, offering the potential to surpass classical machine learning in certain tasks. This paper explores the application of hierarchical feature extraction, a core concept within QML, to enhance the performance of machine learning models. We propose a quantum algorithm for feature extraction that leverages quantum superposition and entanglement to identify and represent complex patterns within data, followed by a quantum neural network to learn from these representations. This approach aims to improve model accuracy and accelerate training through the integration of quantum computation and machine learning. The core claim is that the combined approach will yield superior results compared to traditional machine learning methods. This work addresses the limitations of current machine learning techniques by introducing a novel quantum approach to feature representation, ultimately facilitating a more robust and efficient learning process. We present a theoretical framework and initial implementation demonstrating the potential of this hybrid architecture.
Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, execution time, and noise tolerance. The study examined three leading quantum machine learning (QML) frameworks PennyLane (v0.24), Qiskit (v0.43.2), and TensorFlow Quantum (TFQ, v0.7) each tested on a 64-bit Ubuntu Linux system with an Intel Core i7 processor, 32 GB RAM, and NVIDIA GeForce RTX 3080 GPU. Quantum algorithms like variational quantum circuits (VQCs) and quantum support vector machines (QSVMs) were simulated using these frameworks. Various quantum gates, including Pauli-X, Y, Z, Hadamard, CNOT, and Rotation gates (Rx, Ry, Rz), were used for parameter optimization and quantum superposition. The Iris and MNIST datasets were adapted for quantum encoding using Amplitude and Angle Encoding for binary classification tasks. Simulations were run on classical computers, while select circuits were executed on IBM Quantum’s Falcon r5.11 processor to compare hardware performance. PennyLane achieved the highest accuracy (92%) in quantum simulations, excelling in hybrid quantum-classical model integration. TensorFlow Quantum provided the fastest execution time, especially in classical simulations, making it suitable for rapid prototyping. Qiskit produced deeper quantum circuits but had higher error rates (6%) on real quantum hardware, highlighting noise and decoherence effects. The VQC models optimized by stochastic gradient descent (SGD) showed significant improvements in model performance, and QSVMs effectively classified both linearly separable and non-separable datasets, demonstrating the potential of quantum algorithms over classical approaches. Statistical analysis confirmed the significance of the results (p < 0.05). PennyLane is the most robust framework for hybrid quantum-classical models, while TensorFlow Quantum is suited for rapid prototyping. Qiskit, despite longer execution times, excels in hardware implementation. The choice of framework should align with the specific needs of quantum machine learning tasks.
Abstract Variational quantum circuits (VQC) lie at the forefront of quantum machine learning research. Still, the use of quantum networks for real data processing remains challenging as the number of available qubits cannot accommodate a large dimensionality of data—if the usual angle encoding scenario is used. To achieve dimensionality reduction, Principal Component Analysis is routinely applied as a pre-processing method before the embedding of the classical features on qubits. In this work, we propose an alternative method which reduces the dimensionality of a given dataset by taking into account the specific quantum embedding that comes after. This method aspires to make quantum machine learning with VQCs more versatile and effective on datasets of high dimension. At a second step, we propose a quantum-inspired classical autoencoder model which can be used to encode information in low latent spaces. The power of our proposed models is exhibited via numerical tests. We show that our targeted dimensionality reduction method considerably boosts VQC’s performance, and we also identify cases for which the second model outperforms classical autoencoders in terms of reconstruction loss.