This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study's findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.
This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study's findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.
This research project embarks on a comprehensive comparative analysis of classical and quantum machine learning models specifically tailored for time series forecasting. By employing a range of classical machine learning models alongside quantum machine learning models, the study aims to predict future data points with heightened accuracy and efficiency. Leveraging AWS Braket, the project facilitates the deployment of quantum models on both quantum simulators and quantum hardware devices, providing a robust platform for empirical evaluation. The goal is to meticulously assess the performance metrics, computational efficiency, and predictive accuracy of these models. Ultimately, the research aspires to enhance the understanding and practical application of quantum machine learning techniques in the realm of time series forecasting, potentially uncovering new paradigms in predictive analytics. Our research indicates that the performance metrics for classical machine learning models yielded superior results compared to those of quantum machine learning models for our use case, although when ensembled with classical models, we get better results.
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.
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.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
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
The exponential growth of complex datasets has exposed the limitations of classical machine learning algorithms in speed, scalability, and pattern detection capabilities. Quantum Machine Learning (QML) integrates quantum computation principles—including superposition, entanglement, and quantum interference—into machine learning pipelines to accelerate data processing and optimize pattern recognition. This paper analyzes the theoretical foundations, algorithmic frameworks, performance advantages, hardware feasibility, and real-world applications of QML models, particularly in high-complexity systems such as genomics, financial analytics, cybersecurity, and climate modeling. Through analytical comparisons, datasets, and quantum simulator performance benchmarks, results demonstrate that QML models outperform classical ML by factors ranging from 10× to 1000× in processing speed and accuracy for high-dimensional nonlinear datasets. Critical challenges including qubit instability, data encoding overheads, and hardware scalability are discussed along with emerging solutions like variational quantum circuits and hybrid quantum-classical architectures. The findings establish QML as a foundational technology for future artificial intelligence advancements in ultra-large-scale data environments.