Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Computer Technology and Science

The Role Of Quantum Computing in Optimizing Machine Learning Algorithms

Nattapong Chaiyathorn, Pimchanok Anuwat

Published Apr 30, 2024• 0 citations
Publisher

Abstract

Quantum computing has the potential to revolutionize machine learning by offering exponential speed-up for specific algorithms. This study explores the theoretical and practical implications of using quantum computing to optimize machine learning models, such as in training neural networks. The findings provide insights into the possible improvements in computational efficiency, particularly for large datasets and complex models.

DOI
10.62951/ijcts.v1i2.64

Related papers

Exploring the Impact of Quantum Computing on Machine Learning Performance

Keqin Li, Peng Zhao, Shuying Dai +6 more

This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.

Middle East Journal of Applied Science & Technology•Jan 1, 2024•12 citations

Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms

Sonia Rani, Ravinder Kaur -, Chitra G. Desai +1 more

The paper "Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms" explores the integration of quantum computing principles into classical machine learning techniques, aiming to address limitations such as scalability and computational inefficiency. It presents the foundational concepts of quantum computing, including superposition and entanglement, and their application in accelerating machine learning processes. The study emphasizes the potential for quantum algorithms to significantly improve the performance of machine learning tasks by processing large datasets more efficiently and exploring larger hypothesis spaces. Key quantum machine learning algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNN), each of which leverages quantum mechanics to overcome the computational barriers faced by classical algorithms. The Quantum Approximate Optimization Algorithm (QAOA) is also highlighted for its ability to optimize machine learning models more effectively. While the theoretical benefits of Quantum Machine Learning (QML) are promising, the practical application of these techniques is currently limited by the constraints of existing quantum hardware. This research contributes to the emerging field of QML by examining its potential advantages and future implications in addressing complex data processing challenges.

International Journal For Multidisciplinary Research•Sep 13, 2024•3 citations

The Role of Quantum Computing in Addressing the Big Data Problem in AI with Scaling Machine Learning Models: A Study

Sr.Software Engineer, ANZ Operations and Technology, Bengaluru, Sivananda Lahari Reddy Elicherla, V. Ravindra Reddy

The exponential growth of big data in artificial intelligence (AI) has posed significant challenges for scaling machine learning (ML) models efficiently. Classical computing methods struggle to process vast datasets and optimize complex ML models in a feasible timeframe. Quantum computing offers a promising paradigm shift by leveraging quantum mechanics to perform computations at unprecedented scales and speeds. This study explores the role of quantum computing in addressing the big data problem in AI, with a focus on its potential to enhance the scalability of ML models. Key topics include quantum-enhanced data processing, optimization of large-scale algorithms, and advancements in quantum machine learning (QML). We analyze the theoretical benefits of quantum approaches, such as faster matrix inversion and efficient kernel methods, alongside practical developments in quantum hardware and hybrid classical-quantum frameworks. The study further highlights the challenges, such as noise, hardware limitations, and the gap between theoretical and practical implementations. By bridging quantum computing with AI, this research identifies avenues for accelerating innovation in scalable ML, paving the way for breakthroughs in data-intensive applications. KEYWORDS: Quantum computing, artificial intelligence, big data, machine learning, scalability, quantum machine learning (QML), data processing, optimization, hybrid quantum-classical frameworks

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT•Apr 10, 2025•0 citations

Quantum Computing Enhancements in Machine Learning Algorithms

Rida Ali

This research investigates the potential of quantum computing to improve machine learning algorithms, specifically focusing on optimization tasks. By leveraging quantum superposition and entanglement, we propose a novel hybrid algorithm significantly reducing computation time for large datasets. The results indicate a dramatic increase in speed and accuracy for classification and clustering problems, demonstrating that quantum-enhanced machine learning can outperform traditional approaches. This work paves the way for future explorations into practical applications of quantum machine learning.

Journal of computing and artificial intelligence•Jun 25, 2024•1 citation

Quantum-Enhanced Machine Learning Model Optimization

Jincheng Zhang

Quantum-Enhanced Machine Learning Model Optimization explores the potential of leveraging quantum algorithms to dramatically improve the training process of machine learning models, particularly in parameter optimization and hyperparameter selection. This paper investigates the application of quantum algorithms, specifically quantum approximate optimization algorithms (QAOA), to accelerate the training of complex models. The core claim is that utilizing quantum computing will yield substantial improvements in model performance and efficiency compared to traditional machine learning techniques. The research will detail the process of applying QAOA to a specific model type and will present results demonstrating the potential for enhanced training speed and improved model accuracy. This work offers a novel approach to optimizing machine learning models using quantum principles.

•Sep 3, 2026•0 citations

Analysis on Role of Quantum Computing in Machine Learning

Binju Saju, Madhwaraj Kango Gopal, B. Nithya +2 more

Quantum machine learning is a process by which quantum computers are used to learn from data. It is still in its begining stages of development, but has the potential to be much more efficient than classical machine learning algorithms. The main advantages of machine learning is that it can exploit the massive parallelism of quantum computers. This means that quantum machine learning algorithms can potentially learn from data much faster than classical algorithms. Another advantage is that quantum machine learning algorithms can deal with data that is too large or too complex for classical algorithms. For example, a quantum algorithm could be used to learn from a dataset that is too large to fit into a classical computer's memory. There are still many challenges to overcome before quantum machine learning can be used in practice, but the potential benefits are huge. If successful, Quantum machine learning could revolutionize the field of machine learning and have a profound impact on many other areas of science and technology.

International Conference on Cognitive Computing and Information Processing•Dec 23, 2022•5 citations
Quantum computing has the potential to revolutionize machine learning by offering exponential speed-up for specific algorithms. This study explores the theoretical and practical implications of using quantum computing to optimize machine learning models, such as in training neural networks. The findings provide insights into the possible improvements in computational efficiency, particularly for large datasets and complex models.
This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.
The paper "Quantum Machine Learning: Leveraging Quantum Computing for Enhanced Learning Algorithms" explores the integration of quantum computing principles into classical machine learning techniques, aiming to address limitations such as scalability and computational inefficiency. It presents the foundational concepts of quantum computing, including superposition and entanglement, and their application in accelerating machine learning processes. The study emphasizes the potential for quantum algorithms to significantly improve the performance of machine learning tasks by processing large datasets more efficiently and exploring larger hypothesis spaces. Key quantum machine learning algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNN), each of which leverages quantum mechanics to overcome the computational barriers faced by classical algorithms. The Quantum Approximate Optimization Algorithm (QAOA) is also highlighted for its ability to optimize machine learning models more effectively. While the theoretical benefits of Quantum Machine Learning (QML) are promising, the practical application of these techniques is currently limited by the constraints of existing quantum hardware. This research contributes to the emerging field of QML by examining its potential advantages and future implications in addressing complex data processing challenges.
The exponential growth of big data in artificial intelligence (AI) has posed significant challenges for scaling machine learning (ML) models efficiently. Classical computing methods struggle to process vast datasets and optimize complex ML models in a feasible timeframe. Quantum computing offers a promising paradigm shift by leveraging quantum mechanics to perform computations at unprecedented scales and speeds. This study explores the role of quantum computing in addressing the big data problem in AI, with a focus on its potential to enhance the scalability of ML models. Key topics include quantum-enhanced data processing, optimization of large-scale algorithms, and advancements in quantum machine learning (QML). We analyze the theoretical benefits of quantum approaches, such as faster matrix inversion and efficient kernel methods, alongside practical developments in quantum hardware and hybrid classical-quantum frameworks. The study further highlights the challenges, such as noise, hardware limitations, and the gap between theoretical and practical implementations. By bridging quantum computing with AI, this research identifies avenues for accelerating innovation in scalable ML, paving the way for breakthroughs in data-intensive applications. KEYWORDS: Quantum computing, artificial intelligence, big data, machine learning, scalability, quantum machine learning (QML), data processing, optimization, hybrid quantum-classical frameworks
This research investigates the potential of quantum computing to improve machine learning algorithms, specifically focusing on optimization tasks. By leveraging quantum superposition and entanglement, we propose a novel hybrid algorithm significantly reducing computation time for large datasets. The results indicate a dramatic increase in speed and accuracy for classification and clustering problems, demonstrating that quantum-enhanced machine learning can outperform traditional approaches. This work paves the way for future explorations into practical applications of quantum machine learning.
Quantum-Enhanced Machine Learning Model Optimization explores the potential of leveraging quantum algorithms to dramatically improve the training process of machine learning models, particularly in parameter optimization and hyperparameter selection. This paper investigates the application of quantum algorithms, specifically quantum approximate optimization algorithms (QAOA), to accelerate the training of complex models. The core claim is that utilizing quantum computing will yield substantial improvements in model performance and efficiency compared to traditional machine learning techniques. The research will detail the process of applying QAOA to a specific model type and will present results demonstrating the potential for enhanced training speed and improved model accuracy. This work offers a novel approach to optimizing machine learning models using quantum principles.
Quantum machine learning is a process by which quantum computers are used to learn from data. It is still in its begining stages of development, but has the potential to be much more efficient than classical machine learning algorithms. The main advantages of machine learning is that it can exploit the massive parallelism of quantum computers. This means that quantum machine learning algorithms can potentially learn from data much faster than classical algorithms. Another advantage is that quantum machine learning algorithms can deal with data that is too large or too complex for classical algorithms. For example, a quantum algorithm could be used to learn from a dataset that is too large to fit into a classical computer's memory. There are still many challenges to overcome before quantum machine learning can be used in practice, but the potential benefits are huge. If successful, Quantum machine learning could revolutionize the field of machine learning and have a profound impact on many other areas of science and technology.