International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 10 | Oct 2025
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Leveraging Quantum Computing to Enhance Machine Learning: A Unified Framework for Enhanced Data Processing and Evaluation *Abdullah Faheem1 1SICAS Liberty Complex, Lahore, Pakistan
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Abstract – Quantum Machine Learning (QML) applies
The first combination of ML with Quantum Computing began with the development of QML and the creation of an interdisciplinary field that seeks to apply quantum physics fundamentals to the improvement and optimization of ML models [5]. Unlike the classical approaches, all QML techniques involving classification, clustering, regression, and generative modeling utilize quantum algorithms that can accomplish these tasks considerably quicker and more proficiently [6]. Many frameworks and libraries in quantum computing have made it more accessible to incorporate quantum computing into machine learning workflows. Quantum computing frameworks, including PennyLane, TensorFlow Quantum, Qiskit Machine Learning, and Cirq, offer advanced capabilities for designing and implementing quantum algorithms that run on quantum simulators and real quantum computers [7].
quantum computing methods to advance certain machine learning approaches. Compared to traditional machine learning, QML harnesses quantum superposition and entanglement phenomena to create algorithms with significantly improved processing speeds. Research in quantum machine learning focuses on enhanced support vector machines, quantum neural networks, and variational quantum algorithms tailored to hybrid classical and quantum systems. In this realm, QML focuses on the use of quantum computing during the training of generative models in machine learning technologies, particularly during the optimization of gradient descent. The growing accessibility of machine learning libraries, like PennyLane, TensorFlow Quantum, Qiskit Machine Learning, and Cirq, has streamlined the process of building and implementing systems in QML. The advancements predicted in this new discipline of QML, particularly in computing, finance, healthcare, and materials science, will likely result in improved forecasting and efficiency. Current limitations of quantum computing, such as qubit decoherence, classical data incompatibilities and hybrid quantum-classical models supported with quantum computing infrastructure expansion, positively support the future of practical QML.
One might consider the construction of advanced neural networks as a prime illustration of this. Quantum algorithms possess the capacity to refine and enhance performance metrics across a multitude of dimensions and a high-level feature space [8]. Most of the improvements associated with machine learning have stemmed form the ability of automated systems to refine results with no need for programmed instructions. Machine learning incorporates predictive systems which automate decision making processes, and perform data analysis, thereby unveiling hidden patterns within the data [9]. Applications of artificial intelligence include predictive analytics, medical diagnostics, medical image analysis, and natural language processing. Machine learning technologies have advanced to the point where artificial intelligence is used in many more facets of life than before [10]. This study focuses on the use of machine learning and quantum technology, analyzing how it will affect machine learning and every discipline where data is the core element. regarding every discipline where data is the core element. the discipline where data is the core element. The goal is to derive a unified foundational model for any and all technology processing and computation.
Key Words: Quantum Machine Learning, Quantum Computing, Variational Quantum Algorithms, Quantum Neural Networks, Hybrid Quantum-Classical Models, Data Optimization.
1.INTRODUCTION Quantum Machine Learning (QML) is a new area of study that integrates machine learning and quantum principles. Like any other emergent field of study, it is concentrating on technology-centered advancements within the computational structures augmented by the principles of quantum mechanics to the highest degree possible [1]. QC has a substantially different way of calculating order of magnitudes complex problems, and furthermore, does so much more rapidly than classical computing [2]. This is prominently exhibited with Shor’s quantum factoring algorithm [3], which outpaces classical algorithms and Grover’s search algorithm which retrieves data from a database faster than classical algorithms, providing a quadratic speed acceleration for searching large databases [4].
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1.1 QUANTUM MACHINE LEARNING QML represents the intersection of quantum computing, machine learning, and various other dimensions of QC, seeking advancements in identification and pattern recognition technologies. It aims to address conflicts regarding the most advanced forms of conventional
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