{
  "id": "1912.07286",
  "version": "v1",
  "published": "2019-12-16T10:43:59.000Z",
  "updated": "2019-12-16T10:43:59.000Z",
  "title": "Variational Quantum Circuits for Quantum State Tomography",
  "authors": [
    "Yong Liu",
    "Dongyang Wang",
    "Shichuan Xue",
    "Anqi Huang",
    "Xiang Fu",
    "Xiaogang Qiang",
    "Ping Xu",
    "He-Liang Huang",
    "Mingtang Deng",
    "Chu Guo",
    "Xuejun Yang",
    "Junjie Wu"
  ],
  "comment": "6 pages, 3 figures",
  "categories": [
    "quant-ph",
    "cs.LG",
    "physics.comp-ph"
  ],
  "abstract": "We propose a hybrid quantum-classical algorithm for quantum state tomography. Given an unknown quantum state, a quantum machine learning algorithm is used to maximize the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the variational quantum circuit grows linearly with the number of qubits and the circuit depth. After that, a subsequent classical algorithm is used to reconstruct the unknown quantum state. We demonstrate our method by performing numerical simulations to reconstruct the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator. Our method is suitable for near-term quantum computing platforms, and could be used for relatively large-scale quantum state tomography for experimentally relevant quantum states.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-12-16T10:43:59.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "unknown quantum state",
      "one-dimensional quantum spin chain",
      "variational quantum circuit simulator",
      "relatively large-scale quantum state tomography",
      "variational quantum circuit grows"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}