Abstract Quantum machine learning (QML) has emerged as an innovative framework that has the potential to uncover complex patterns by leveraging the ability of quantum systems to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quantum hardware, as unitary rotations introduce overhead from complex number computations, and quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce a quantum parallel information exchange hybrid network, a new non-sequential hybrid classical quantum model architecture that leverages quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits. This enables efficient pattern recognition and temporal series data prediction by utilizing non-Clifford parameterized quantum gates, thereby enhancing both learning efficiency and representational capacity. Additionally, we developed a dynamic gradient selection method that applies the parameter-shift rule to quantum processing units (QPUs) and adjoint differentiation to graphics processing units (GPUs). Our results demonstrate that the model performance exhibits higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100 -steps, and showing a more unbiased eigenvalue spectrum of the Fisher information matrix on the CPU/GPU and IonQ QPU simulators.
Abstract Quantum machine learning (QML) has emerged as an innovative framework that has the potential to uncover complex patterns by leveraging the ability of quantum systems to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quantum hardware, as unitary rotations introduce overhead from complex number computations, and quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce a quantum parallel information exchange hybrid network, a new non-sequential hybrid classical quantum model architecture that leverages quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits. This enables efficient pattern recognition and temporal series data prediction by utilizing non-Clifford parameterized quantum gates, thereby enhancing both learning efficiency and representational capacity. Additionally, we developed a dynamic gradient selection method that applies the parameter-shift rule to quantum processing units (QPUs) and adjoint differentiation to graphics processing units (GPUs). Our results demonstrate that the model performance exhibits higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100 -steps, and showing a more unbiased eigenvalue spectrum of the Fisher information matrix on the CPU/GPU and IonQ QPU simulators.
This research explores the integration of quantum computing with classical machine learning to enhance data classification tasks using Quantum Neural Networks (QNN) and Parameterized Quantum Circuits (PQC). The hybrid approach leverages the advantages of both quantum and classical systems to improve the efficiency and accuracy of data processing. In this model, data is encoded into qubits using amplitude encoding, representing input vectors as amplitudes of quantum states. The QNN is initialized by placing the qubits in superposition using Hadamard gates, followed by data encoding with parameterized rotational gates that map classical data to quantum states using rotation angles. PQC plays a central role by applying layers of parameterized quantum operations to process data in the quantum space. These parameters are optimized during the training process, where a quadratic loss function minimizes the error between the predicted quantum states and the true class labels using gradient descent. Experiments conducted on the MNIST dataset show that the hybrid the hybrid quantum-classical neural network (QCNN) with PQC achieves a classification accuracy of over 95%, highlighting its potential in machine learning applications. The results demonstrate that integrating quantum computing with classical machine learning enhances performance in complex data analysis tasks due to the exponential growth of quantum state space and the parallelism of quantum systems, making hybrid models promising for computer vision and classification tasks.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.
This chapter explores quantum machine learning (QML) and neural network transfer learning. It begins by describing the potential of QML. The discussion then shifts to transfer learning, leveraging pre-trained neural models across diverse domains. A demonstration of advancements in both fields forms the core of the chapter, showcasing how QML classifiers can be used with classical neural networks for enhanced performance. To improve the accuracy of COVID-19 screening, ensemble method and sliding window mechanism measurements have been employed using computer vision on frequency domain spectrograms of audio files. Parallel with this, the accuracy of these measurements could be improved by quantum machine transfer learning. The chapter describes a case study where a hybrid approach demonstrated significant improvements in data processing accuracy, offering an understanding of practical applications. In conclusion, the authors present ideas on how the combination of QML and transfer learning could unfold new horizons in various fields with complex, large-scale datasets. The chapter concludes with predictions about the trajectory of these technologies, emphasizing their role in shaping the future of transfer learning. This combination of current research and visionary thinking inspires further exploration at the intersection of quantum computing machine learning and neural network transfer learning.
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 computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
Abstract Quantum Machine Learning (QML) is a promising field that combines the power of quantum computing with machine learning. Variational quantum circuits, where parameters of circuits are learned classically, have been widely used in many recent applications of QML. This is an instance of a hybrid quantum-classical framework, where both classical and quantum components are present. However, applying these techniques to applications involving massive data is a challenging task. One way to overcome this, is using the concept of classical-quantum transfer learning with the help of dressed quantum circuit, introduced recently, where the underlying neural architecture is pre-trained classically, but at the final steps (decision layer), a quantum circuit is used, followed by quantum measurements and post-processing to classify images with high precision. In this paper, we applied hybrid classical-quantum transfer learning to another task of massive data processing, i.e. Natural Language Processing (NLP). We show how to (binary) classify short texts (e.g., SMS) with classical-quantum transfer learning, which was originally applied to image processing only. Our quantum network was pre-trained by Bidirectional Encoder Representations from the Transformers (BERT) model, and its variational quantum circuit is fine-tuned for text processing. We evaluated the performance of our hybrid neural architecture using the Receiver Operating Characteristic (ROC) curve, which is typically used in the evaluation of classification problems. The results indicate high precision as well as lower loss function. To our knowledge, our work is the first application of quantum transfer learning to the area of NLP. Finally a comparison with a tool that uses learning but in a different way than transfer learning is presented