A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead

• URL: http://arxiv.org/abs/2310.10315v2
• Date: Sat, 27 Jul 2024 08:08:45 GMT
• Title: A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
• Authors: Kamila Zaman, Alberto Marchisio, Muhammad Abdullah Hanif, Muhammad Shafique,
• Abstract summary: 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.
• Score: 5.629434388963902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 paper serves as a valuable resource for readers seeking to understand the current state-of-the-art techniques in the QML field, offering a solid foundation to embark on further exploration and development in this rapidly evolving area.

Related papers

• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Security Concerns in Quantum Machine Learning as a Service [2.348041867134616]
  Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML models can effectively generalize from limited training data samples. QML represents a hybrid model that utilizes both classical and quantum computing resources.
  arXiv  Detail & Related papers  (2024-08-18T18:21:24Z)
• Feature Importance and Explainability in Quantum Machine Learning [0.0]
  Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. This article explores feature importance and explainability in Quantum Machine Learning (QML) compared to Classical ML models.
  arXiv  Detail & Related papers  (2024-05-14T19:12:32Z)
• Predominant Aspects on Security for Quantum Machine Learning: Literature Review [0.0]
  Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review.
  arXiv  Detail & Related papers  (2024-01-15T15:35:43Z)
• Foundations of Quantum Federated Learning Over Classical and Quantum Networks [59.121263013213756]
  Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. QFL can be deployed over both classical and quantum communication networks.
  arXiv  Detail & Related papers  (2023-10-23T02:56:00Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Quantum-Inspired Machine Learning: a Survey [32.913044758923455]
  Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers. This survey explores QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning.
  arXiv  Detail & Related papers  (2023-08-22T08:29:09Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Classification with Quantum Machine Learning: A Survey [17.55390082094971]
  We combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) This paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML)
  arXiv  Detail & Related papers  (2020-06-22T14:05:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.