{
  "id": "2206.06290",
  "version": "v1",
  "published": "2022-06-13T16:21:04.000Z",
  "updated": "2022-06-13T16:21:04.000Z",
  "title": "Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer",
  "authors": [
    "Pradeep Niroula",
    "Ruslan Shaydulin",
    "Romina Yalovetzky",
    "Pierre Minssen",
    "Dylan Herman",
    "Shaohan Hu",
    "Marco Pistoia"
  ],
  "comment": "16 pages, 7 figure, 1 table",
  "categories": [
    "quant-ph",
    "cs.ET"
  ],
  "abstract": "Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on the Quantinuum H1-1 trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 765 two-qubit gates with a two-qubit gate depth of up to 159, and all 20 qubits of the H1-1 device. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. Our experimental results demonstrate that the rapid hardware and algorithmic progress is enabling the solution of constrained-optimization problems on quantum hardware.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-06-13T16:21:04.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "trapped-ion quantum computer",
      "constrained quantum optimization",
      "extractive summarization",
      "alternating operator ansatz algorithm",
      "constrained-optimization problem"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 16,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}