Quantum-Dot Neuromorphic Edge AI for Ultra-Secure IoT and Brain-Inspired Computing

Quantum-Dot Neuromorphic Edge AI for Ultra-Secure IoT and Brain-Inspired Computing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 December 2025 V1 Latest version Share on Quantum-Dot Neuromorphic Edge AI for Ultra-Secure IoT and Brain-Inspired Computing Authors : Pushkar Sharma 0009-0009-8326-2519 [email protected] , Ashwini Mali , Payal Panigrahi , Sandhyarani Dora , Damini Suryavanshi , and Khushboo Jangid Authors Info & Affiliations https://doi.org/10.22541/au.176529685.59484231/v1 233 views 141 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In order to achieve ultra-secure, adaptive, and low-power intelligence for distributed IoT environments, this paper presents a next-generation neuromorphic edge architecture that combines quantum-dot nanomaterials with spiking neural computation. Because of their multi-level tunability and quantum-confined electronic structure, quantum dots (QDs) serve as nanoscale synaptic elements that can produce intrinsic randomness, stable conductance states, and extremely low-energy switching. Real-time on-chip learning and event-driven spike computation are made possible by neuromorphic processors, which are based on biological neural systems. By utilizing QD-based entropy sources and physically unclonable functions (PUFs), the proposed Quantum-Dot Neuromorphic Edge AI Processor (QD-NEAP) reduces vulnerabilities related to traditional IoT devices and eliminates cloud dependency. When compared to CMOS and memristor-based architectures, performance evaluation shows notable gains in energy efficiency, inference latency, and cryptographic strength. This work creates a single braininspired computational platform that combines secure IoT communication, neuromorphic learning, and quantum-dot materials. Supplementary Material File (quantum-dot_neuromorphic_edge_ai_for_ultra-secure_iot_and_brain-inspired_computing.pdf) Download 218.56 KB Information & Authors Information Version history V1 Version 1 09 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords brain-inspired hardware edge ai low-power intelligence nanoscale synapses physically unclonable functions (pufs) quantum entropy quantum-dot neuromorphic computing secure iot spiking neural networks (snns) Authors Affiliations Pushkar Sharma 0009-0009-8326-2519 [email protected] ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Ashwini Mali ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Payal Panigrahi ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Sandhyarani Dora ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Damini Suryavanshi ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Khushboo Jangid ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University View all articles by this author Metrics & Citations Metrics Article Usage 233 views 141 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pushkar Sharma, Ashwini Mali, Payal Panigrahi, et al. Quantum-Dot Neuromorphic Edge AI for Ultra-Secure IoT and Brain-Inspired Computing. Authorea . 09 December 2025. DOI: https://doi.org/10.22541/au.176529685.59484231/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176529685.59484231/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffc0dad8ad506eb',t:'MTc3OTQ1NTM0Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();