Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms.
Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms.
Quantum Machine Learning (QML) is an exciting frontier applying the philosophy of quantum computation to classical machine learning for improved learning performance of complicated tasks. However, QML is greatly challenged by realistic issues like noisy quantum measurements and limited labeled quantum data caused by the high cost and technological limitations. This paper explores the novel transfer-learning mechanisms to tackle these issues, which allow the reuse of the knowledge from related quantum tasks for improving model performance and generalization in resource-limited scenarios. Transfer learning, one of the great and successful concepts of classical machine learning, is to transfer the learned parameters or features from the source domain (or task) to the target domain (or task). We further develop and investigate, in quantum, a methodology where parameterized quantum circuits (PQCs) learnt on a source quantum data set have certain parts partially re-used or fine-tuned for a target task, given limited amount of target data. This strategy also avoids large quantum training runs that are not scalable on state-of-the-art noisy intermediate-scale quantum (NISQ) processors. The suggested methods are layerwise quantum adaptation, quantum kernel reuse and hybrid quantum-classical feature transfer. We investigate the robustness of these mechanisms in presence of noise by simulating quantum circuits using depolarizing and readout noise models. Moreover, we show that pretraining of quantum models on synthetic (or simulated) data sets (which are generated using larger, less noisy systems) can be instrumental in learning on real, small-sized quantum devices. Experiment results in quantum classification tasks demonstrate the effectiveness of transfer learning in boosting the accuracy, accelerating the convergence, and lowering the quantum circuit depth of successfully trained model. It is interesting to mention that transfer using layer-wise models became very resistant to quantum noise, as its performance did not degrade significantly for noisy inputs. These results demonstrate that transfer learning can overcome the scarce quantum data and suppress the harmful impact of measurement noise. This work presents a potential venue for enhancing the applicability of QML in the NISQ era. By transferring knowledge from theoretical quantum learning models to actual quantum hardware constraints, transfer learning allows quantum algorithms to learn faster and generalize better across tasks.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
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) offers a paradigm shift in machine learning, leveraging the unique properties of quantum mechanics to potentially surpass classical algorithms. This paper explores the application of QML to adaptive transfer learning, a technique designed to dynamically adjust model parameters based on the data distribution and feature characteristics. We propose a novel quantum framework for this process, utilizing quantum entanglement to establish a stronger connection between training data and model parameters. This approach aims to address the limitations of traditional transfer learning, particularly in scenarios with complex data distributions and variable feature spaces. The core mechanism focuses on creating a dynamic, self-adjusting model that can generalize better. This work introduces a preliminary quantum algorithm demonstrating the potential for improved transfer learning performance, highlighting the benefits of quantum entanglement in this context.
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.
Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data. In this study, we introduce the notion of quantum adversarial transfer learning, where data are completely encoded by quantum states. A measurement-based judgment of the data label and a quantum subroutine to compute the gradients are discussed in detail. We also prove that our proposal has an exponential advantage over its classical counterparts in terms of computing resources such as the gate number of the circuits and the size of the storage required for the generated data. Finally, numerical experiments demonstrate that our model can be successfully trained, achieving high accuracy on certain datasets.