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Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures

Osama M. Nayfeh
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--> Quantum Physics arXiv:2607.11992 (quant-ph) [Submitted on 13 Jul 2026] Title:Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures Authors:Osama M. Nayfeh View a PDF of the paper titled Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures, by Osama M. Nayfeh View PDF Abstract:Hardware neurons incorporating built-in memory components are critical technologies for implementing large scale neural networks that exhibit adaptive-itinerant behavior and produce biological-inspired spiking patterns that match neuroscience known operations (1).
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Quantum Physics arXiv:2607.11992 (quant-ph) [Submitted on 13 Jul 2026] Title:Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures Authors:Osama M. Nayfeh View a PDF of the paper titled Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures, by Osama M. Nayfeh View PDF Abstract:Hardware neurons incorporating built-in memory components are critical technologies for implementing large scale neural networks that exhibit adaptive-itinerant behavior and produce biological-inspired spiking patterns that match neuroscience known operations (1). Moreover, neurons capable of full quantum information processing through their qubit states and quantum trajectory provides capability for hybrid quantum-classical operations through the level of coherence/entanglement and non-Markovianity (2,3). These attributes further contribute to solving complex quantum mechanical problems by providing a rich and diverse group for expressing quantum neural states. Upgrading these hardware neurons now with optical spin qubits excitable with pulsed laser excitation in the near infrared and designed from negatively charged silicon vacancies in semiconductor silicon carbide (4) and Neodymium rare earth ions in the superconducting Niobium/Niobium oxide film (5) that form the memory provides access to the unique spin Hamiltonian that considers electronic and nuclear interactions. In this manuscript, we experimentally measure opto-electronic quantum-classical neurons integrated with a optical spin qubit and examine how tailored neuronal spiking sequences drive the photoluminescence oscillations and modulate the quantum spin transitions. We derive a quantum model for the system that considers the interaction between the neuron and optical qubit spin and perform calculations to project how quantum computing operations are expandable across the state space in the quantum-classical neuron and spin qubit system. For example, the resulting impact on the neuron quantum states where the strength is adjustable due to dynamical memory changes. These results are critical for realizing hardware neurons with quantum optical capabilities and inspired by the existence in biology of biophotons. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.11992 [quant-ph] (or arXiv:2607.11992v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.11992 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Osama Nayfeh [view email] [v1] Mon, 13 Jul 2026 15:46:12 UTC (2,264 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures, by Osama M. NayfehView PDF view license Current browse context: quant-ph new | recent | 2026-07 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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