{
  "id": "2306.09984",
  "version": "v1",
  "published": "2023-06-16T17:28:35.000Z",
  "updated": "2023-06-16T17:28:35.000Z",
  "title": "Variational quantum algorithms for machine learning: theory and applications",
  "authors": [
    "Stefano Mangini"
  ],
  "comment": "Final thesis for the Ph.D in Physics at the University of Pavia, 220 pages",
  "categories": [
    "quant-ph"
  ],
  "abstract": "This Ph.D. thesis provides a comprehensive review of the state-of-the-art in the field of Variational Quantum Algorithms and Quantum Machine Learning, including numerous original contributions. The first chapters are devoted to a brief summary of quantum computing and an in-depth analysis of variational quantum algorithms. The discussion then shifts to quantum machine learning, where an introduction to the elements of machine learning and statistical learning theory is followed by a review of the most common quantum counterparts of machine learning models. Next, several novel contributions to the field based on previous work are presented, namely: a newly introduced model for a quantum perceptron with applications to recognition and classification tasks; a variational generalization of such a model to reduce the circuit footprint of the proposed architecture; an industrial use case of a quantum autoencoder followed by a quantum classifier used to analyze classical data from an industrial power plant; a study of the entanglement features of quantum neural network circuits; and finally, a noise deconvolution technique to remove a large class of noise when performing arbitrary measurements on qubit systems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-06-16T17:28:35.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "variational quantum algorithms",
      "applications",
      "quantum machine learning",
      "quantum neural network circuits",
      "noise deconvolution technique"
    ],
    "tags": [
      "dissertation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 220,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}