{
  "id": "2309.13961",
  "version": "v1",
  "published": "2023-09-25T08:53:54.000Z",
  "updated": "2023-09-25T08:53:54.000Z",
  "title": "A hybrid quantum-classical approach to warm-starting optimization",
  "authors": [
    "Vanessa Dehn",
    "Thomas Wellens"
  ],
  "comment": "11 pages, 6 figures",
  "categories": [
    "quant-ph"
  ],
  "abstract": "The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting the standard algorithm improves the performance. In this paper we compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization and investigate the warm-start approach for different problem instances. In particular, we analyze the extent to which the improved performance of warm-start QAOA is due to quantum effects, and show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-09-25T08:53:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "hybrid quantum-classical approach",
      "warm-starting optimization",
      "quantum approximate optimization algorithm",
      "standard qaoa",
      "warm-start qaoa"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}