This study provides insights into the current limitations of quantum machine learning compared to classical machine learning and identifies areas for future research. We present a novel approach that utilizes real IBM quantum computers to classify celestial objects within the extensive Sloan Digital Sky Survey Data Release 18 (SDSS-V DR18) dataset. Despite persistent challenges in both hardware and software, quantum computers are being explored as tools for enhancing machine learning performance in comparison to classical methods due to potential upside. This investigation delves into the untapped potential of quantum machine learning and quantum neural networks in tackling the complexities of processing vast telescope data. By leveraging quantum technologies, we aim to expedite the analysis of large complex data, unveiling hidden patterns, and propelling specialized fields such as astronomical research into the quantum era.
This study provides insights into the current limitations of quantum machine learning compared to classical machine learning and identifies areas for future research. We present a novel approach that utilizes real IBM quantum computers to classify celestial objects within the extensive Sloan Digital Sky Survey Data Release 18 (SDSS-V DR18) dataset. Despite persistent challenges in both hardware and software, quantum computers are being explored as tools for enhancing machine learning performance in comparison to classical methods due to potential upside. This investigation delves into the untapped potential of quantum machine learning and quantum neural networks in tackling the complexities of processing vast telescope data. By leveraging quantum technologies, we aim to expedite the analysis of large complex data, unveiling hidden patterns, and propelling specialized fields such as astronomical research into the quantum era.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.
The rapid advancement of quantum computing hardware and software has ushered in a new era for computational science, promising to tackle problems that are intractable for classical computers. Parallelly, machine learning (ML) has revolutionized data analysis and artificial intelligence. The confluence of these two fields, known as Quantum Machine Learning (QML), aims to harness the principles of quantum mechanics-such as superposition, entanglement, and interference-to enhance and redefine classical machine learning algorithms. This survey provides a comprehensive overview of the burgeoning field of QML. We begin by elucidating the fundamental quantum concepts that underpin QML approaches. We then present a detailed taxonomy and analysis of prominent QML algorithms, categorizing them into quantum-enhanced classical models and fully quantum models. Key families of algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and Quantum Generative Adversarial Networks (QGANs). For each, we explain the theoretical quantum advantage, the circuit implementation, and the current limitations. Furthermore, we delve into the critical challenges facing the practical realization of QML, including the pervasive issue of noise in Noisy Intermediate-Scale Quantum (NISQ) devices, the problem of barren plateaus in training quantum circuits, and the complexities of efficient data encoding (quantum feature maps). We also present a small-scale experimental simulation of a variational quantum classifier on a standardized dataset, demonstrating its principle and comparing its performance with a classical counterpart. Finally, the paper discusses the future trajectory of QML, highlighting the importance of error mitigation, hybrid quantum-classical architectures, and the development of quantum-specific learning inductive biases. We conclude that while fault-tolerant quantum computers remain on the horizon, QML represents a paradigm shift with the potential to deliver profound computational advantages in the coming decades.
Classification is a fundamental aspect of leveraging big data for decision-making across domains such as engineering, medicine, economics, and beyond. This systematic review explores the application of quantum machine learning (QML) to address the classification challenges. Despite the rising popularity of QML, challenges persist regarding its practical efficacy and applicability relative to classical methods. This study seeks to classify and analyse the application of QML across diverse domains. Employing a systematic methodology, a total of 23 research articles were chosen after applying rigorous selection criteria between the years 2013 to 2023. At first, 520 research articles were identified, and narrowed down to 296 papers, the abstracts of the papers have been read, 61 full-text papers have been studied, and ultimately 23 papers have been selected for review. A methodological approach was employed, involving the selection of relevant papers from reputable databases, evaluation of these papers, and the subsequent derivation of insights into the performance of QML in practical classification applications. Findings indicate that QML classifiers mostly achieved superior performance compared to conventional methods across accuracy, high-dimensional data management, and computational efficiency. However, challenges remain, including the limitations of noisy quantum hardware and the requirement for hybrid and innovative quantum algorithms. Analysis of current trends reveals significant progress in hybrid quantum-classical computational methods. To conclude, this analysis reveals QML’s revolutionary capabilities while noting its current shortcomings. This study provides a thorough overview for researchers investigating QML’s application to real-world classification problems, and it offers guidelines for future research endeavors.
Quantum Machine Learning (QML) is an advanced discipline that emerges from the combined power of machine learning and quantum computing that has the ability to address intricate challenges in several domains. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. This research delves into the fundamental principles of quantum mechanics and their crucial role in quantum computing, emphasizing the potential of various quantum algorithms to surpass classical algorithms in specific computational tasks, and then methodically navigates through the quantum machine learning algorithms, offering profound insights into their application potential in revolutionizing data analysis and complex problem-solving methodologies, including their importance in the Language Learning Models (LLM) and Language Analysis Models (LAM). The study also provides insights into the various quantum platforms, encompassing both hardware and software aspects for the implementation of QML algorithms, and also explores the challenges prevalent in QML, with a particular focus on the limitations imposed by existing quantum hardware and the intricate nuances of data processing within quantum frameworks. This study contributes by presenting the basis for future research work related to the development of algorithms in the field of quantum machine learning and anticipating the far-reaching impact of QML across diverse scientific and technological domains.