{
  "id": "2402.16616",
  "version": "v1",
  "published": "2024-02-26T14:47:13.000Z",
  "updated": "2024-02-26T14:47:13.000Z",
  "title": "Quantum process tomography of structured optical gates with convolutional neural networks",
  "authors": [
    "Tareq Jaouni",
    "Francesco Di Colandrea",
    "Lorenzo Amato",
    "Filippo Cardano",
    "Ebrahim Karimi"
  ],
  "doi": "10.1088/2632-2153",
  "categories": [
    "quant-ph"
  ],
  "abstract": "The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learning techniques have been proposed. Here, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators, only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated on complex polarization transformations. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-02-26T14:47:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum process tomography",
      "convolutional neural network",
      "structured optical gates",
      "process entire experimental images",
      "complex polarization transformations"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "IOP"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}