{
  "abstract": "A promising route to superconducting artificial neural networks is a hybrid digital-Analog architecture that combines digital single-flux-quantum (SFQ) communication with compact Analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-Analog-to-digital (DAD) converter, in which the role of the Analog cell was performed by a $Σ$-neuron with a nonlinear transfer function -- the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an Analog-to-digital converter (ADC), and re-encodes the Analog $Σ$-neuron waveforms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $Σ$-neuron, and mapped back to pulse-based outputs. As a key experimental step, we fabricated and characterised a redesigned $Σ$-neuron and measured a sigmoid-like transfer characteristic suitable for activation-function implementation. The extracted response was incorporated into system-level simulations to assess the influence of realistic device parameters on the conversion process. We delineate the operating-range matching requirements for the DAC, neuron, and ADC blocks, supporting the feasibility of the proposed interface as a building block for perceptron-like superconducting neural networks with digital inputs and outputs. Finally, we developed two perceptron networks, one using a mathematical sigmoid activation and the other the measured $Σ$-neuron transfer characteristic, which reached classification accuracies of $97.0\\%$ and $91.9\\%$, respectively, on the MNIST handwritten digit dataset.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.21613",
  "author_total": 10,
  "authors": [
    {
      "name": "Andrey E. Schegolev"
    },
    {
      "name": "Vsevolod I. Ruzhickiy"
    },
    {
      "name": "Georgy I. Gubochkin"
    },
    {
      "name": "Alexander S. Ionin"
    },
    {
      "name": "Ivan A. Nazhestkin"
    },
    {
      "name": "Mikhail Y. Fominskii"
    },
    {
      "name": "Lyudmila V. Filippenko"
    },
    {
      "name": "Igor I. Soloviev"
    },
    {
      "name": "Maxim V. Tereshonok"
    },
    {
      "name": "Nikolay V. Klenov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3r4h4qzcyxdg9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.21613",
  "fulltext_url": null,
  "id": "3r4h4qzcyxdg9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.21613",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.supr-con"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.21613",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3r4h4qzcyxdg9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2609.21613",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.21613"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Digital-analog concept for superconducting perceptron-like neural networks",
  "title_text": "Digital-analog concept for superconducting perceptron-like neural networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:03Z",
  "url": "https://research.draconitic.com/paper/3r4h4qzcyxdg9",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/y84t-zzch",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}