Quantum machine Learning (QML) is an emerging technique that leverages quantum theory and the yet-to-be fully developed quantum computers. QML may help solve classification problems that cannot be resolved by deep neural network (DNN). However, QML suffers from a quantum aliasing problem that is created by the inevitable downsampling and binarization operations. QML takes classical data domain as its input, transforms it into a domain of quantum states (a subset of the Hilbert space) using quantum encoding, generates quantum feature vectors, and builds a quantum circuit on the quantum feature vectors—as a machine learning model—to perform classification tasks. Hence, quantum encoding is an important task in QML. However, in addition to the quantum aliasing problem created by the downsampling and binarization operations in the quantum encoding of the classical data to quantum bits, the data scarcity can intensify this problem into an uncontrollable state. This problem has been rarely studied in the QML research literature. Therefore, it is important to study this problem in depth and develop a robust quantum encoding scheme that improves the performance of QML under the influence of data scarcity. In this work, the performance instability of QML under the severity of quantum aliasing and the influence of data scarcity (a combined effect of data and quantum paucity) has been studied by using the TensorFlow Quantum software framework and the MNIST handwritten digits dataset. A quantum encoding—by leveraging Fast Fourier Transform (FFT) and Blackman-Harris window—that encodes the data into qubits to reduce quantum aliasing has been proposed. An empirical study shows that QML significantly improves its learning behavior and performs better than DNN under data scarcity with the use of the proposed FFT-based quantum encoding.
Quantum machine Learning (QML) is an emerging technique that leverages quantum theory and the yet-to-be fully developed quantum computers. QML may help solve classification problems that cannot be resolved by deep neural network (DNN). However, QML suffers from a quantum aliasing problem that is created by the inevitable downsampling and binarization operations. QML takes classical data domain as its input, transforms it into a domain of quantum states (a subset of the Hilbert space) using quantum encoding, generates quantum feature vectors, and builds a quantum circuit on the quantum feature vectors—as a machine learning model—to perform classification tasks. Hence, quantum encoding is an important task in QML. However, in addition to the quantum aliasing problem created by the downsampling and binarization operations in the quantum encoding of the classical data to quantum bits, the data scarcity can intensify this problem into an uncontrollable state. This problem has been rarely studied in the QML research literature. Therefore, it is important to study this problem in depth and develop a robust quantum encoding scheme that improves the performance of QML under the influence of data scarcity. In this work, the performance instability of QML under the severity of quantum aliasing and the influence of data scarcity (a combined effect of data and quantum paucity) has been studied by using the TensorFlow Quantum software framework and the MNIST handwritten digits dataset. A quantum encoding—by leveraging Fast Fourier Transform (FFT) and Blackman-Harris window—that encodes the data into qubits to reduce quantum aliasing has been proposed. An empirical study shows that QML significantly improves its learning behavior and performs better than DNN under data scarcity with the use of the proposed FFT-based quantum encoding.
Quantum machine learning (QML) models operating on classical data require an encoding step to map information into the Hilbert space. However, this imposes a computational bottleneck absent in classical frameworks, necessitating rigorous theoretical complexity analysis and algorithmic mitigations. We provide theoretical foundations of different encoding methods that prepare quantum states for both simulators and hardware, analyzing normal and sparse high energy physics data. In addition, we propose a quantum data augmentation method based on randomly sampled circuits to improve sample efficiency and mitigate the data scarcity bottleneck. We demonstrate the effectiveness of this method on a generative quantum diffusionlike model to produce synthetic samples of classical images. Our results demonstrate that choosing domain optimal encoding and applying the novel quantum data augmentation leads to faster convergence and improved model performance, offering a highly sample-efficient and scalable approach to encoding and augmenting sparse classical data for QML.
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
When it comes to realizing immediate quantum advantages in practical applications like machine learning and optimization techniques, numerous methods have emerged as a frontrunner technique. Such algorithms often involve quantum circuits for data encoding and the training of Quantum Neural Networks (QNNs) to minimize target functions when applied to tasks involving classical data. Quantum Machine Learning (QML) is a promising area of quantum computing because it may effectively handle difficult learning tasks by taking advantage of the high dimensional Hilbert space to train better representations from limited data. Despite QML's rising popularity, few academic works have addressed the language's security features. There has been a lot of buzz about the promising new discipline of quantum machine learning. Training a parameterized quantum circuit is a common practice in contemporary QML for performing data analysis on classical or quantum datasets. To make accurate predictions on an independent test dataset, state-of-the-art QML techniques first variationally optimize a parameterized quantum circuit using the training dataset. Quantum modeling in QML has also been shown to offer an exponential benefit in sample complexity. In the realms of pattern recognition, machine learning, and data mining, feature dimensionality reduction as a crucial link in the process has become one hot and tough spot. Most academics have gravitated towards this topic because of the difficulty of its studies. The goal of this research is to learn how to implement low loss in the procedure of feature dimension reduction, preserve the integrity of the original data, locate the most effective mapping, and obtain the best possible low-dimensional data. Preliminary findings suggest that QML models may offer advantages over classical models for classical data analysis. In this paper, a thorough investigation into generalization performance in QML is performed. This paper presents a brief survey on Qubits Encoding using Quantum Machine Learning models that help researchers to identify the limitations of the existing models and to design new efficient models for better performance levels.
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Quantum Machine Learning (QML) and Quantum Deep Learning (QDL) are emerging research areas that combine quantum computing with machine learning and deep learning techniques.These approaches aim to improve computational speed, feature representation, optimisation, and learning performance in complex data-driven problems.This review paper presents a structured discussion of recent developments in QML and QDL, focusing on major algorithms, application areas, implementation approaches, current limitations, and future research directions.The review covers important quantum-based learning methods, including quantum support vector machines, quantum k-nearest neighbours, quantum principal component analysis, quantum neural networks, variational quantum classifiers, quantum convolutional neural networks, quantum reinforcement learning, quantum generative models, and hybrid quantum-classical models.The paper also discusses how these methods are being explored in healthcare, finance, cybersecurity, communication networks, image processing, natural language processing, and optimisation.However, the review shows that most QML and QDL models are still in the early stages of research.Current challenges include noisy quantum hardware, limited qubit count, data encoding issues, circuit design problems, difficulties with model training, and a lack of standardised benchmarks.The review concludes that QML and QDL have strong future potential, but their practical success depends on improved hardware, stronger algorithms, reliable frameworks, and fair comparison with classical models.
Quantum Machine Learning (QML) represents an emerging interdisciplinary field that harnesses quantum computing principles to enhance machine learning algorithms and develop quantum-native learning paradigms. This convergence exploits fundamental quantum mechanical phenomena superposition, entanglement, and quantum interference—to potentially achieve exponential computational advantages over classical approaches for specific algorithmic tasks. The theoretical foundation of QML rests on quantum systems' ability to encode information in exponentially scaling state spaces, enabling complex data representation in logarithmically fewer qubits compared to classical bits. Key quantum advantage mechanisms include quantum parallelism through superposition states, entanglement based correlations for efficient encoding of data relationships, and quantum interference effects that amplify optimal solutions while suppressing suboptimal ones. Contemporary QML approaches encompass several algorithmic frameworks. Variational Quantum Algorithms (VQAs) represent the most promising near-term strategy, utilizing hybrid quantum-classical optimization with parameterized quantum circuits optimized through classical feedback loops. Quantum Neural Networks extend classical architectures into quantum domains using trainable quantum gates, while quantum kernel methods leverage quantum feature maps to project data into high-dimensional Hilbert spaces for enhanced classification and regression tasks.