Quantum machine learning (QML) holds immense potential for revolutionizing computational intelligence, yet faces significant challenges in optimizing quantum neural network architectures for practical implementation. This paper introduces Q-Drop, an innovative quantum adaptation technique that enhances parameterized quantum circuits (PQCs) for training performance. The system includes two novel optimization strategies: statistical pruning and dynamic dropout (circuit flipflops), which significantly improve the learning performance and robustness of quantum orthogonal neural networks. Evaluation results across standard (MNIST, Fashion-MNIST) and medical imaging (Pneumonia-MNIST, Retina-MNIST) datasets demonstrate substantial performance gains. Notably, the approach achieves up to 94.3 % accuracy on medical image classification and 98.7 % accuracy on standard image datasets, outperforming existing quantum machine learning methods. By addressing critical challenges in quantum neural network training, this work highlights the potential of applying quantum machine learning to enhance performance for classical computer vision tasks and QML-driven scheduling optimization in quantum networks.
Quantum machine learning (QML) holds immense potential for revolutionizing computational intelligence, yet faces significant challenges in optimizing quantum neural network architectures for practical implementation. This paper introduces Q-Drop, an innovative quantum adaptation technique that enhances parameterized quantum circuits (PQCs) for training performance. The system includes two novel optimization strategies: statistical pruning and dynamic dropout (circuit flipflops), which significantly improve the learning performance and robustness of quantum orthogonal neural networks. Evaluation results across standard (MNIST, Fashion-MNIST) and medical imaging (Pneumonia-MNIST, Retina-MNIST) datasets demonstrate substantial performance gains. Notably, the approach achieves up to 94.3 % accuracy on medical image classification and 98.7 % accuracy on standard image datasets, outperforming existing quantum machine learning methods. By addressing critical challenges in quantum neural network training, this work highlights the potential of applying quantum machine learning to enhance performance for classical computer vision tasks and QML-driven scheduling optimization in quantum networks.
Quantum Machine Learning (QML) has been acknowledged for its transformative potential in enhancing computational capabilities beyond classical approaches, with Quantum Neural Networks (QNNs) emerging as one of the most promising models. Despite the advancements, the adaptation of effective optimization techniques such as batch normalization or regularization, which are well-established in classical Neural Networks, remains an ongoing challenge for QNNs.This adaptation is crucial, especially when considering the implementation of techniques like quantum dropout, which, although inspired by its classical counterpart to reduce the risk of overfitting by "deleting" certain model components during the forward step, exhibits distinct behaviours in a quantum context. This study introduces a novel approach to modulate the impact of dropout in QNNs through a technique we term Soft Dropout. Our method introduces a parameterized softening mechanism for gate eliminations, enabling a more nuanced control over the dropout process, thereby mitigating its adverse effects on the network’s learning capacity, and reducing at the same time the risk of overfitting.Our experimental analysis demonstrates that softening current quantum dropout consistently enhances model performance across a spectrum of configurations. This improvement is attributed to the intrinsic properties of quantum gates, which allow for a gradual adjustment of their impact on the quantum circuit up to the identity operation. The results highlight the importance of adapting classical machine learning techniques to the quantum context, considering the unique computational model of quantum processing, potentially accelerating the adoption of QML in real-world problems.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
Quantum machine learning (QML) has demonstrated the potential to outperform classical machine learning algorithms in various fields. However, encoding classical data into quantum states, known as quantum data loading, remains a challenge. Existing methods achieve high accuracy in loading single data, but lack efficiency for large-scale data loading tasks. In this work, we propose Neural Circuit Parameter Prediction, a novel method that leverages classical deep neural networks to predict the parameters of parameterized quantum circuits directly from the input data. This approach benefits from the batch inference capability of neural networks and improves the accuracy of quantum data loading. We introduce real-valued parameterization of quantum circuits and a three-phase training strategy to further enhance training efficiency and accuracy. Experimental results on MNIST dataset show that our method achieves a 17.31 % improvement in infidelity score and 108 times faster runtime compared to existing methods. Our approach provides an efficient solution for quantum data loading, enabling the practical deployment of QML algorithms on large-scale datasets.
Adaptive quantum machine learning (QML) represents an exciting frontier at the intersection of quantum computing and classical bio-inspired optimization. The constraints of noisy intermediate-scale quantum (NISQ) devices—limited qubit counts, significant decoherence, and measurement uncertainties—pose formidable challenges to training parameterized quantum circuits (PQCs) using traditional gradient-based methods. To overcome these hurdles, we propose a novel Adaptive PSO–QML framework that integrates a particle swarm optimization (PSO)–inspired algorithm with dynamic hyperparameter adjustment, enabling real-time tuning of PQC parameters based on performance feedback. Unlike static optimizers, our approach continuously adapts its inertia weight and cognitive/social coefficients to balance exploration and exploitation, responding to stagnation or rapid improvement in measured fitness. We evaluate this hybrid algorithm on two benchmark classification tasks: the classical Iris dataset encoded into a 2-qubit circuit and a quantum-encoded subset of the MNIST dataset on 4 qubits. Our experiments, conducted on the Qiskit Aer simulator under realistic noise models (depolarizing, amplitude damping, and mixed), demonstrate that Adaptive PSO–QML achieves an average accuracy improvement of 12% over fixed-parameter PSO and 15% over Adam-based gradient descent, with p-values below 0.01 across paired t-tests. Statistical analysis summarized in Table 1 confirms the significance of these gains. Detailed simulation research further reveals that the adaptive mechanism not only accelerates convergence by approximately 30%—reducing total circuit evaluations—but also exhibits robust performance across varying circuit depths (1–3 entangling layers) and noise intensities. Our findings suggest that bio-inspired adaptation is a powerful tool for mitigating barren plateaus and noise effects in NISQ-era QML, paving the way for scalable quantum advantage in near-term hardware.
Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.
Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms. The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks. We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.