Quantum machine learning (QML) is an evolving interdisciplinary research field that combines quantum computing with machine learning techniques, which enables more efficient and accurate analysis of complex data (e.g., dataset of COVID-19). In recent years, we have witnessed significant progress and innovative methods at the intersection of neural network transfer learning (NNTL) in various computational diagnosis models of healthcare. This chapter explores the possibility of change at the intersection of NNTL and QML. QML can be used to tackle computationally complex problems. We have emphasized on transfer learning, a method that utilizes pre-trained neural network models to adapt and perform effectively across diverse settings. Some examples of recent developments that combine QML classifiers with traditional neural networks and their effective performance have been illustrated. Moreover, a case study on improving the accuracy of COVID-19 screening has been reported. This approach evaluates audio signal spectrograms in the frequency domain using ensemble approaches and sliding window processes based on computer vision techniques. Quantum-assisted transfer learning is a potential approach for further enhancing the accuracy of the data analysis achieved through such approaches. Further, this chapter shows a hybrid approach, showing significant improvement in data processing accuracy up to 99.05%, thereby manifesting its applicability toward practical concerns. The approach demonstrates the possibilities for the application of QML along with transfer learning together which effectively handles complicated large-scale datasets. The chapter concludes with a thought-provoking conversation on challenges and opportunities for complex data analysis by quantum computing in a regime of global quantum technology landscape. This chapter will provide new insights into quantum computing for the advancement of AI, combing QML and NNTL.
Quantum machine learning (QML) is an evolving interdisciplinary research field that combines quantum computing with machine learning techniques, which enables more efficient and accurate analysis of complex data (e.g., dataset of COVID-19). In recent years, we have witnessed significant progress and innovative methods at the intersection of neural network transfer learning (NNTL) in various computational diagnosis models of healthcare. This chapter explores the possibility of change at the intersection of NNTL and QML. QML can be used to tackle computationally complex problems. We have emphasized on transfer learning, a method that utilizes pre-trained neural network models to adapt and perform effectively across diverse settings. Some examples of recent developments that combine QML classifiers with traditional neural networks and their effective performance have been illustrated. Moreover, a case study on improving the accuracy of COVID-19 screening has been reported. This approach evaluates audio signal spectrograms in the frequency domain using ensemble approaches and sliding window processes based on computer vision techniques. Quantum-assisted transfer learning is a potential approach for further enhancing the accuracy of the data analysis achieved through such approaches. Further, this chapter shows a hybrid approach, showing significant improvement in data processing accuracy up to 99.05%, thereby manifesting its applicability toward practical concerns. The approach demonstrates the possibilities for the application of QML along with transfer learning together which effectively handles complicated large-scale datasets. The chapter concludes with a thought-provoking conversation on challenges and opportunities for complex data analysis by quantum computing in a regime of global quantum technology landscape. This chapter will provide new insights into quantum computing for the advancement of AI, combing QML and NNTL.
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) is an emerging discipline at the intersection of quantum computing and artificial intelligence, providing new approaches to address the increasing computational and analytical challenges in healthcare. This chapter explores how quantum principles, such as superposition, entanglement, and interference, can enhance the efficiency and accuracy of machine learning when applied to complex biomedical data. It presents an overview of the fundamental concepts of QML, compares its capabilities with those of traditional machine learning systems, and examines important algorithmic frameworks, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms. Practical examples are explored across several healthcare domains—drug discovery, medical imaging, genomic research, patient monitoring, and hospital resource management—showing how quantum methods are beginning to improve data interpretation and clinical decision-making. The chapter also outlines current limitations related to hardware capacity, data preparation, interpretability, and regulatory standards, while emphasizing ethical and cybersecurity considerations that accompany quantum-enabled systems. Finally, it looks ahead to a future in which accessible quantum computing, open-source development, and collaboration among scientists, clinicians, and technologists drive a new generation of personalized, secure, and data-driven healthcare.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the computational power of quantum computing with machine learning techniques to address the scalability challenges faced by classical AI systems. As datasets and models grow in complexity, traditional machine learning algorithms encounter limitations related to processing speed, accuracy, and resource efficiency. This research explores the potential of QML to overcome these barriers by leveraging quantum algorithms such as Quantum Approximate Optimization Algorithms (QAOA), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNNs) to enhance the scalability and efficiency of AI applications. Through a combination of literature review, algorithm simulation, and hybrid quantum-classical approaches, the study evaluates the speedup, accuracy improvements, and resource efficiency that quantum-enhanced models can offer. The research also investigates practical applications of QML in domains such as healthcare, finance, and climate science. Despite significant advancements, challenges related to quantum hardware, error correction, and noise remain, which will be discussed alongside strategies for overcoming these issues. The findings suggest that while quantum machine learning holds immense potential, its full impact on scalable AI will require further advancements in quantum computing and its integration with classical systems.
Abstract Quantum machine learning (QML) is a rapidly growing interdisciplinary field at the intersection of quantum computing and machine learning, aiming to harness the unique properties of quantum mechanics to address complex computational challenges and enhance algorithmic performance. Despite its potential, QML faces significant challenges such as computational complexity, data efficiency, and resource requirements. This paper introduces a novel transfer learning approach to improve the performance and efficiency of quantum models. By leveraging the knowledge from previously trained quantum models, our method reduces training time and enhances accuracy, making QML more practical for real-world applications. Experimental results validate the effectiveness of our approach, demonstrating substantial improvements in both accuracy and training time. PACS numbers: 03.67.Ac,03.67.Lx,03.67.Mn,03.67.Pp
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum computing and artificial intelligence (AI) to overcome the limitations of classical machine learning algorithms.This paper explores the foundational principles of quantum computing and their application in enhancing machine learning algorithms.The researcher discuss the potential of quantum speedup, new possibilities in data classification and optimization, and hardware and algorithm development challenges.The paper also highlights the interdisciplinary applications of QML in financial modeling, logistics, supply chain optimization, and drug discovery.Through qualitative and quantitative analysis, we provide insights into the current state of QML, limitations, and future research directions.Quantum computing, with its ability to process information in fundamentally new ways, offers a paradigm shift in computational power.By leveraging quantum mechanical phenomena such as superposition, entanglement, and interference, QML aims to solve problems that are currently intractable for classical computers.This paper delves into the theoretical underpinnings of QML, examining how quantum algorithms can be integrated with classical machine learning techniques to achieve superior performance.The researcher also explore the practical implications of QML in various industries, emphasizing its potential to revolutionize fields such as finance, logistics, and healthcare.Despite the promising advancements, significant challenges remain, including the development of robust quantum hardware and the creation of scalable quantum algorithms.This paper provides a comprehensive overview of the current state of QML, identifies key research gaps, and outlines future directions for this rapidly evolving field.
Quantum computing is rapidly advancing and offering new ways to improve artificial intelligence (AI). The most recent advancement is Quantum Machine Learning (QML), an exciting new field that involves the exploration of combining quantum computing principles with machine learning methods to solve complex problems that have high-dimensional problem-solving models that are impossible to address with classical models. The objective of this research was a descriptive-analytical examination of combining classical machine learning with quantum computing principles, specifically using Variational Quantum Classifiers (VQC) to train on a nonlinear XOR dataset, to assess the proposition of QML. The VQC was able to amongst quantum features, and learning how to classify using parameterized variational circuits to model nonlinear decision boundaries as models not achievable using classical linear classifiers. Training convergence was demonstrated with decreasing training and test losses, with the final model achieving test accuracy of nearly 0.85. The training curves and the decision boundary plots illustrated the model’s ability to learn the more complex decision boundaries. This study demonstrated QML's ability to coexist with classical machine learning studies with the ability to exploit quantum entanglement and quantum parallelism. This paper concluded that QML has great potential to challenge the concerns of next generation applications in finance, healthcare, and secure communications and to optimize computational power and scalability.