{
  "id": "2310.10315",
  "version": "v1",
  "published": "2023-10-16T11:52:54.000Z",
  "updated": "2023-10-16T11:52:54.000Z",
  "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"
  ],
  "categories": [
    "quant-ph",
    "cs.LG"
  ],
  "abstract": "Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is applied to Machine Learning (ML) applications, it forms a Quantum Machine Learning (QML) system. After discussing the basic concepts of QC and its advantages over classical computing, this paper reviews the key aspects of QML in a comprehensive manner. We discuss different QML algorithms and their domain applicability, quantum datasets, hardware technologies, software tools, simulators, and applications. In this survey, we provide valuable information and resources for readers to jumpstart into the current state-of-the-art techniques in the QML field.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-10-16T11:52:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum machine learning",
      "current trends",
      "road ahead",
      "challenges",
      "opportunities"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}