{
  "annotation_id": "5c16e230-a247-4050-a6cf-961a4dd18867",
  "date_created": "2026-02-17T05:53:15.748000Z",
  "date_modified": "2026-02-17T05:53:15.748000Z",
  "file_hash": "edb56d02db01898d40c7e13f69ca6d122dfcd89b3ae0fb94c1c1ac146cf15bad",
  "private": false,
  "record": {
    "abstract": "Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources.",
    "arxiv_id": "2601.08213",
    "authors": [
      "Anastasiia Butko",
      "Artem Marisov",
      "David I. Santiago",
      "Irfan Siddiqi"
    ],
    "categories": [
      "quant-ph",
      "cs.ET"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology",
    "url": "https://arxiv.org/abs/2601.08213",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "05bfb65d-4fc8-422c-a840-c72b3c0b9380",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}