QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI

# QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI
Source: [https://arxiv.org/html/2605.27729](https://arxiv.org/html/2605.27729)
Aoyu Zhang†Luyao Zhang†\*†Equal contributions\. Authors are listed in alphabetical order by last name and then first name\.\*Corresponding author: Luyao Zhang \(lz183@duke\.edu\), Digital Innovation Research Center and Social Science Division, Duke Kunshan University\. Address: Duke Avenue No\.8, Kunshan, Suzhou, Jiangsu, China, 215316\.

###### Abstract

The 2024–2025 Nobel and Turing awards recognised artificial intelligence and quantum science in the same breath — machine learning as a physical science, artificial intelligence solving 50\-year scientific problems, superconducting quantum circuits as the hardware foundation of quantum computing, and quantum information principles as computing’s highest achievement\. Yet no deployed artificial intelligence system has brought these two streams together for the general public: identity systems still rely on pseudo\-random tokens, and quantum circuits remain invisible to the billions of people who use bot\-enabled social messaging platforms daily\. This paper presents QSignAI, a production\-deployed open\-source platform demonstrating a bidirectional relationship between artificial intelligence and quantum science in a real\-time event participation system\. We address three research questions: first, can quantum\-randomness generation via independent quantum measurements condensed by a two\-source extractor be embedded in an AI\-driven social platform with acceptable latency and cost; second, can an AI bot make quantum phenomena perceptually legible to general audiences with no prior technical knowledge; and third, does a system combining both directions work in practice? A conversational AI bot routes each participant’s first message through a quantum pipeline comprising a Toeplitz two\-source extractor over independent single\-qubit Hadamard measurements on SV1 and DM1 simulators, plus a 2\-qubit Bell state, producing a unique quantum\-randomness\-seeded identity signature per participant\. The current deployment uses cloud quantum simulators; physical quantum randomness from a quantum processing unit is the near\-term extension\. The first two research questions are answered through system architecture and qualitative deployment evidence from live events; the third through successful production deployment\. Measurable latency benchmarks and controlled user studies are identified as priority future work\.

★\\bigstarParticipation Layer→\\rightarrowBot\-enabled messaging platform \(Telegram, 1B\+ MAU\)⋅\\cdot@mention→\\toWebhook→\\toQSignAIAPI⋆\\starBot / API Layer□\\squareECS Fargate⋅\\cdot✓\\checkmarkWebhook validation⋅\\cdot→\\rightarrowMention detection⋅\\cdot■\\blacksquarePhoto pipeline⋅\\cdotsignatureStatus = generating★\\bigstarQuantum LayerAWS Braket SV1 \+ DM1⊳\\trianglerightCkt A: Two\-source QRNG⊳\\trianglerightToeplitz extractor⊳\\trianglerightCkt B: Bell state\|Φ\+⟩\|\\Phi^\{\+\}\\rangle⊳\\trianglerightToyLWE signature◆\\blacklozengeData LayerDynamoDB&S3⊳\\trianglerightMessages, positions⊳\\trianglerightQuantum signatures⊳\\trianglerightPhotos, Braket results∘\\circPresentation LayerCloudFrontCDN⊳\\trianglerightNext\.js wall⊳\\triangleright5\-second polling⊳\\trianglerightDrag\-and\-drop↯ HTTPS webhookasync dispatch⋅\\cdotread\-write★\\bigstarsig\. \+ nonce◆\\blacklozengesigned URLsScience→\\toAIAI→\\toScienceFigure 1:System architecture of QSignAI showing the bidirectional relationship between AI and quantum science\.Layers:■\\blacksquareParticipation\(bot\-enabled messaging, Telegram 1B\+ MAU\);■\\blacksquareBot/API\(ECS Fargate, webhook, photo pipeline\);■\\blacksquareQuantum\(SV1 \+ DM1→\\toToeplitz extractor→\\toToyLWE signatures\);■\\blacksquareData\(DynamoDB \+ S3\);■\\blacksquarePresentation\(CloudFront, Next\.js wall\)\.Directions:Sci\.→\\toAI\(quantum randomness strengthens identity\);AI→\\toSci\.\(bot makes quantum legible to audiences\)\.## IIntroduction

In 2024–2025, the Nobel and Turing committees recognised artificial intelligence \(AI\) and quantum science simultaneously\. The 2024 Nobel Prize in Physics went to Hopfield and Hinton\[[11](https://arxiv.org/html/2605.27729#bib.bib13)\]for foundational discoveries enabling machine learning with artificial neural networks — the first time the Nobel Committee in Physics recognised AI research\. The 2024 Nobel Prize in Chemistry recognised Hassabis, Jumper, and Baker\[[12](https://arxiv.org/html/2605.27729#bib.bib15)\]for AlphaFold2, the canonical demonstration of AI for Science\. The 2025 Nobel Prize in Physics recognised Clarke, Devoret, and Martinis\[[14](https://arxiv.org/html/2605.27729#bib.bib12)\]for macroscopic quantum tunnelling and energy quantisation in electrical circuits — the direct physical foundation of today’s superconducting quantum computers\. The 2025 ACM Turing Award — computing’s highest honour — recognised Bennett and Brassard\[[4](https://arxiv.org/html/2605.27729#bib.bib10)\]for establishing the foundations of quantum information science, including the principle that quantum measurement produces randomness no classical adversary can reproduce\.

Despite this convergence, three gaps persist\.Gap 1 \(Science for AI\):AI identity systems still use pseudo\-random number generators \(PRNGs\) — deterministic algorithms that are theoretically reversible\. Bennett and Brassard’s Turing Award\-winning insight has not been applied to AI participation systems\.Gap 2 \(AI for Science\):quantum circuits remain invisible to non\-specialists\. AlphaFold2 showed AI can make science accessible at scale\[[12](https://arxiv.org/html/2605.27729#bib.bib15)\]; can an AI bot do the same for quantum science?Gap 3 \(AI for Better Life\):transformation only reaches people when it is accessible\. A bot on a platform billions already use — zero install, zero technical knowledge — is a concrete answer to how quantum science improves everyday life\.

This paper addresses three Research Questions \(RQs\):

- •RQ1\(Science for AI\):Can quantum\-randomness generation via independent quantum measurement sources condensed by a two\-source extractor be embedded in an AI\-driven social participation system with acceptable latency, cost, and reliability?
- •RQ2\(AI for Science\):Can an AI bot make quantum phenomena — superposition, entanglement, Bell state measurement — perceptually legible to general audiences with no prior technical knowledge?
- •RQ3\(Deployment\):Does a system combining both directions work in practice, and what does successful deployment demonstrate about the bidirectional relationship?

The contributions of this paper are as follows\. We present a complete, production\-deployed system demonstrating the bidirectional AI–quantum relationship, including quantum circuit design comprising a two\-source random number generator \(QRNG\) based on independent single\-qubit Hadamard measurements on SV1 and DM1 simulators, plus a 2\-qubit Bell state on Amazon Web Services \(AWS\) Braket SV1 \(state\-vector simulator\), seeding ToyLWE \(Toy Learning\-With\-Errors\) identity signatures\. We describe the Telegram Bot API integration architecture with multi\-group isolation and graceful degradation, and a three\-surface User Experience \(UX\) making quantum\-derived identity legible to general audiences\. The open\-source implementation is available on GitHub111https://github\.com/QuantBlockchain/QSignAI\.

The present work focuses on production deployment, post\-quantum cryptographic identity binding, and two\-source extractor theory; the companion paper*Quantum Futures Interactive*\[[13](https://arxiv.org/html/2605.27729#bib.bib3)\]emphasises educational game design, player\-experience evaluation, and museum\-scale interactive installations\. Both share the AWS Braket and serverless infrastructure stack but have distinct contributions\.

## IIBackground and Related Work

### II\-AQuantum Randomness

Classical PRNGs are deterministic: given the same seed, they always produce the same sequence\. A quantum measurement is governed by the Born rule — outcomes are determined by probability amplitudes, not hidden variables, as experimentally confirmed by Aspect et al\.\[[2](https://arxiv.org/html/2605.27729#bib.bib7)\]closing the Bell inequality loophole established by Bell\[[3](https://arxiv.org/html/2605.27729#bib.bib6)\]\. Cloud\-accessible quantum randomness is now available via services such as AWS Braket\. NIST SP 800\-90B\[[16](https://arxiv.org/html/2605.27729#bib.bib9)\]defines randomness requirements for cryptographic applications\. The key distinction in this paper: quantum randomness is the*input*\(what the circuits produce\); identity signatures are the*output*\(what ToyLWE derives\)\.

### II\-BPost\-Quantum\-Inspired Signatures

The NIST post\-quantum cryptography \(PQC\) standardisation process\[[17](https://arxiv.org/html/2605.27729#bib.bib18)\]produced CRYSTALS\-Dilithium \(FIPS 204\[[18](https://arxiv.org/html/2605.27729#bib.bib19)\]\), based on the Learning With Errors \(LWE\) problem introduced by Regev\[[20](https://arxiv.org/html/2605.27729#bib.bib17)\]\. LWE mixes a secret vector with a noise vector, producing a distribution computationally indistinguishable from uniform\. ToyLWE \(Toy Learning\-With\-Errors\) as used in this work is a demonstrative, pedagogical instantiation — structural analogy to LWE, not a production primitive\. The path to production is replacement with CRYSTALS\-Dilithium \(FIPS 204, ML\-DSA\)\[[18](https://arxiv.org/html/2605.27729#bib.bib19)\]using the same quantum\-randomness\-seeded entropy pipeline

### II\-CBot\-Enabled Social Messaging Platforms

Bot\-enabled group messaging platforms expose a programmable webhook application programming interface \(API\) enabling AI agents to mediate between human participants and backend systems\. The current deployment uses Telegram, which surpassed 1 billion monthly active users \(MAU\) in March 2025\[[8](https://arxiv.org/html/2605.27729#bib.bib29)\]\. Prior bot applications span education, healthcare, and civic engagement\[[9](https://arxiv.org/html/2605.27729#bib.bib22)\], but none combine quantum circuits with real\-time social display\. Existing interactive event platforms \(Slido, Mentimeter\) use PRNG tokens with no quantum provenance\.

### II\-DAI for Science

The AlphaFold2 result\[[12](https://arxiv.org/html/2605.27729#bib.bib15)\]established the template for AI for Science: an AI system making a scientific problem accessible at a scale classical methods cannot match\. QSignAI applies the same template to quantum science communication: an AI bot making quantum phenomena accessible to a live audience of non\-specialists\. Quantum machine learning\[[5](https://arxiv.org/html/2605.27729#bib.bib20)\]and Noisy Intermediate\-Scale Quantum \(NISQ\) Era computing\[[19](https://arxiv.org/html/2605.27729#bib.bib21)\]provides the broader context for near\-term quantum applications\.

## IIISystem Architecture

The overall architecture is shown in Fig\.[1](https://arxiv.org/html/2605.27729#S0.F1)\. The system comprises three integrated layers\.

TheParticipation Layeris a bot\-enabled group messaging platform\. The current deployment uses Telegram’s Bot API222Telegram Bot API:[https://core\.telegram\.org/bots/api](https://core.telegram.org/bots/api)with webhook delivery validated by𝖷​\-​𝖳𝖾𝗅𝖾𝗀𝗋𝖺𝗆​\-​𝖡𝗈𝗍​\-​𝖠𝗉𝗂​\-​𝖲𝖾𝖼𝗋𝖾𝗍​\-​𝖳𝗈𝗄𝖾𝗇\\mathsf\{X\\text\{\-\}Telegram\\text\{\-\}Bot\\text\{\-\}Api\\text\{\-\}Secret\\text\{\-\}Token\}\. Mention detection parses𝗆𝖾𝗌𝗌𝖺𝗀𝖾\.𝖾𝗇𝗍𝗂𝗍𝗂𝖾𝗌\\mathsf\{message\.entities\}for𝗍𝗒𝗉𝖾:”​𝗆𝖾𝗇𝗍𝗂𝗈𝗇​”\\mathsf\{type\{:\}\\ "mention"\}with offset/length extraction;𝖼𝖺𝗉𝗍𝗂𝗈𝗇​\_​𝖾𝗇𝗍𝗂𝗍𝗂𝖾𝗌\\mathsf\{caption\\\_entities\}handles photo messages\. Photos are downloaded via the platform file API \(20 MB limit\) and uploaded to a private, encrypted Amazon Simple Storage Service \(S3\) bucket\.333AWS S3:[https://docs\.aws\.amazon\.com/s3/](https://docs.aws.amazon.com/s3/)TheQuantum Identity Layerruns on AWS Braket SV1444AWS Braket:[https://docs\.aws\.amazon\.com/braket/](https://docs.aws.amazon.com/braket/)and is described in Section[IV](https://arxiv.org/html/2605.27729#S4)\. ThePresentation Layeris a Next\.js555Next\.js:[https://nextjs\.org/](https://nextjs.org/)application served via Amazon CloudFront, polling for new messages every five seconds\.

Infrastructure\.The full stack is defined as infrastructure\-as\-code using AWS Cloud Development Kit \(CDK\)\.666AWS CDK:[https://docs\.aws\.amazon\.com/cdk/](https://docs.aws.amazon.com/cdk/)Elastic Container Service \(ECS\) Fargate runs 1–4 container instances, auto\-scaling at 70% CPU utilisation\. Amazon DynamoDB \(DynamoDB\) stores messages with composite key𝖦𝖱𝖮𝖴𝖯​\#​\{𝗀𝗋𝗈𝗎𝗉𝖨𝖽\}\\mathsf\{GROUP\\\#\\\{groupId\\\}\}/𝖬𝖲𝖦​\#​\{𝗍𝗂𝗆𝖾𝗌𝗍𝖺𝗆𝗉\}​\#​\{𝗆𝖾𝗌𝗌𝖺𝗀𝖾𝖨𝖽\}\\mathsf\{MSG\\\#\\\{timestamp\\\}\\\#\\\{messageId\\\}\}, encrypted at rest with point\-in\-time recovery enabled\. All credentials are stored in AWS Secrets Manager; no secrets appear in code or container images\. Scaling parameters are summarised in Table[II](https://arxiv.org/html/2605.27729#S3.T2)\.

Security\.Multi\-layer security includes CloudFront secret\-header validation blocking direct Application Load Balancer \(ALB\) access, Telegram token validation on every webhook request, bearer token authentication for admin endpoints, HTTP Strict Transport Security \(HSTS\) and Content Security Policy \(CSP\) headers, least\-privilege Identity and Access Management \(IAM\) roles, and HTML entity encoding for all user input \(4096\-character maximum\)\. The system API endpoints are listed in Table[I](https://arxiv.org/html/2605.27729#S3.T1)\.

TABLE I:QSignAI REST API Endpoints\. Icons denote role:★\\bigstaringest,◆\\blacklozengedisplay,×\\timesdelete,∘\\circlayout,∙\\bulletadmin,⊳\\trianglerighthealth\.TABLE II:ECS Fargate Auto\-Scaling Parameters\. Value colors encode scale:■\\blacksquareminimal/stable,■\\blacksquaremoderate\.
## IVQuantum Randomness Pipeline

### IV\-AWhy Quantum Randomness

A PRNG is a deterministic function: given the same seed, it always produces the same output\. An adversary who recovers the seed can reproduce every identity token the system has ever issued\. A quantum measurement has no such algorithm underneath it\. The Born rule states that measurement outcomes are governed by probability amplitudes — not hidden variables, not prior system state\. This was experimentally confirmed by Aspect et al\.\[[2](https://arxiv.org/html/2605.27729#bib.bib7)\]\. Quantum\-randomness\-seeded identity tokens on a physical QPU cannot be predicted or reproduced by any classical adversary, regardless of computational power, because the randomness was not computed — it was measured\[[4](https://arxiv.org/html/2605.27729#bib.bib10),[10](https://arxiv.org/html/2605.27729#bib.bib1)\]\. The present deployment uses classical simulators \(SV1 and DM1\), which are deterministic at the hardware level; the architecture is QPU\-ready and the extractor pipeline applies directly to hardware\-sourced randomness\[[22](https://arxiv.org/html/2605.27729#bib.bib2)\]\.

### IV\-BCircuit A: Two\-Source Quantum RNG

The two quantum circuits are shown in Fig\.[2](https://arxiv.org/html/2605.27729#S4.F2)\. Circuit A implements a two\-source quantum random number generator \(QRNG\) on AWS Braket\. Two independent single\-qubit Hadamard circuits are executed in parallel: one on the SV1 state\-vector simulator \(the*ideal*source\) and one on the DM1 density\-matrix simulator \(the*noisy*source\)\. Each circuit applies a Hadamard gate to place the qubit in superposition, then measures in the computational basis\. Repeating this processnntimes yields two raw bit strings:

- •𝐗∈\{0,1\}n\\mathbf\{X\}\\in\\\{0,1\\\}^\{n\}from SV1, representing the ideal quantum randomness, and
- •𝐘∈\{0,1\}n\\mathbf\{Y\}\\in\\\{0,1\\\}^\{n\}from DM1, capturing physically noisy but independently generated randomness\.

These raw streams are not individually uniform—SV1 is a deterministic simulator and a single\-source modal outcome is predictable—so they are condensed through a Toeplitz two\-source extractor\. LetTTbe anm×nm\\times nbinary Toeplitz matrix whose first row and first column are derived from𝐘\\mathbf\{Y\}\. The extractor computes

𝐙=Ext​\(𝐗,𝐘\)=T⋅𝐗⊕𝐠,\\mathbf\{Z\}\\;=\\;\\mathrm\{Ext\}\(\\mathbf\{X\},\\mathbf\{Y\}\)\\;=\\;T\\cdot\\mathbf\{X\}\\;\\oplus\\;\\mathbf\{g\},\(1\)where𝐠∈\{0,1\}m\\mathbf\{g\}\\in\\\{0,1\\\}^\{m\}is a universal hash offset also derived from𝐘\\mathbf\{Y\}, and all arithmetic is over𝔽2\\mathbb\{F\}\_\{2\}\. The output𝐙∈\{0,1\}m\\mathbf\{Z\}\\in\\\{0,1\\\}^\{m\}isε\\varepsilon\-close to uniform provided both sources have sufficient independent min\-entropy, withm≈n⋅km\\approx n\\cdot kfor min\-entropy ratekk\.

The extractor output is partitioned into two fields:

qnum\\displaystyle q\_\{\\mathrm\{num\}\}=int\(𝐙\[0:⌈log21001⌉\],2\)mod1001,\\displaystyle\\;=\\;\\mathrm\{int\}\\\!\\Bigl\(\\mathbf\{Z\}\\bigl\[0:\\lceil\\log\_\{2\}001\\rceil\\bigr\],2\\Bigr\)\\bmod 001,\(2\)𝐫\\displaystyle\\mathbf\{r\}=𝐙\[⌈log21001⌉:⌈log21001⌉\+256\],\\displaystyle\\;=\\;\\mathbf\{Z\}\\bigl\[\\lceil\\log\_\{2\}001\\rceil:\\lceil\\log\_\{2\}001\\rceil\+56\\bigr\],yieldingqnum∈\[0,1000\]q\_\{\\mathrm\{num\}\}\\in\[0,1000\]and a 32\-byte nonce𝐫∈\{0,1\}256\\mathbf\{r\}\\in\\\{0,1\\\}^\{256\}for replay resistance in the ToyLWE signature \(Eq\.[3](https://arxiv.org/html/2605.27729#S4.E3)\)\.

★\\bigstar\(a\) Circuit A: Two\-Source QRNGSV1 \(ideal\)H→H\\toMeasure𝐗∈\{0,1\}n\\mathbf\{X\}\\in\\\{0,1\\\}^\{n\}DM1 \(noisy\)H→H\\toMeasure𝐘∈\{0,1\}n\\mathbf\{Y\}\\in\\\{0,1\\\}^\{n\}Toeplitz Two\-Source Extractor𝐙=T⋅𝐗⊕𝐠\\mathbf\{Z\}=T\\cdot\\mathbf\{X\}\\oplus\\mathbf\{g\}qnum∈\[0,1000\]q\_\{\\mathrm\{num\}\}\\in\[0,1000\]𝐫∈\{0,1\}256\\mathbf\{r\}\\in\\\{0,1\\\}^\{256\}Raw bit strings𝐗\\mathbf\{X\}\(SV1\) and𝐘\\mathbf\{Y\}\(DM1\) feed a Toeplitz extractor;output𝐙\\mathbf\{Z\}is partitioned intoqnumq\_\{\\mathrm\{num\}\}and nonce𝐫\\mathbf\{r\}\.★\\bigstar\(b\) Circuit B: 2\-qubit Bell State\|Φ\+⟩=12​\(\|00⟩\+\|11⟩\)\|\\Phi^\{\+\}\\rangle=\\tfrac\{1\}\{\\sqrt\{2\}\}\(\|00\\rangle\+\|11\\rangle\)q0q\_\{0\}q1q\_\{1\}H⊕\\oplusMM200 shots→\\to𝜷=\[P​\(\|00⟩\),P​\(\|01⟩\),P​\(\|10⟩\),P​\(\|11⟩\)\]\\bm\{\\beta\}=\[P\(\|00\\rangle\),\\,P\(\|01\\rangle\),\\,P\(\|10\\rangle\),\\,P\(\|11\\rangle\)\]H: Hadamard∙\\bullet–⊕\\oplus: CNOTToeplitzqnumq\_\{\\mathrm\{num\}\}𝐫\\mathbf\{r\}MMeasureFigure 2:Quantum circuits used in QSignAI\.\(a\) Circuit A\(two\-source QRNG\): Source 1 \(SV1, ideal\) and Source 2 \(DM1, noisy\) each execute single\-qubitH→measureH\\to\\text\{measure\}circuits; raw bit strings𝐗\\mathbf\{X\}and𝐘\\mathbf\{Y\}feed a Toeplitz two\-source extractor producing uniform output𝐙\\mathbf\{Z\}, partitioned intoqnum∈\[0,1000\]q\_\{\\mathrm\{num\}\}\\in\[0,1000\]and 32\-byte nonce𝐫\\mathbf\{r\}\.\(b\) Circuit B\(Bell state\|Φ\+⟩\|\\Phi^\{\+\}\\rangle\):■\\blacksquareH \+ CNOT create maximal entanglement; 200 shots→\\to𝜷=\[P​\(00\),…,P​\(11\)\]\\bm\{\\beta\}=\[P\(00\),\\ldots,P\(11\)\], encoding quantum entanglement in each participant’s card colour\.#### Two\-Source Randomness Extraction

A single source of raw bits, even from a quantum simulator, is insufficient for cryptographic\-grade randomness: SV1 is a deterministic state\-vector simulator whose outcome distribution is fully predictable given the same circuit, and taking the modal outcome of a simpleH→measureH\\to\\text\{measure\}circuit would yield a deterministic value rather than a genuinely random one\. To overcome this, QSignAI employs a*two\-source extractor*—a classical post\-processing primitive that combines two*independent*weak random sources into a single output that is statistically close to uniform\.

The two\-source model, formalised by Chor and Goldreich\[[6](https://arxiv.org/html/2605.27729#bib.bib4)\]and substantially developed by Dodis*et al\.*\[[7](https://arxiv.org/html/2605.27729#bib.bib5)\], requires only that each source contains sufficient min\-entropy, with no assumption that either source is fully uniform or that the sources are uncorrelated beyond their independence\. Formally, let\(𝐗,𝐘\)\(\\mathbf\{X\},\\mathbf\{Y\}\)be two independent random variables on\{0,1\}n\\\{0,1\\\}^\{n\}with min\-entropiesH∞​\(𝐗\)≥kH\_\{\\infty\}\(\\mathbf\{X\}\)\\geq kandH∞​\(𝐘\)≥kH\_\{\\infty\}\(\\mathbf\{Y\}\)\\geq k\. A\(n,k,ε\)\(n,k,\\varepsilon\)two\-source extractor is a functionExt:\{0,1\}n×\{0,1\}n→\{0,1\}m\\mathrm\{Ext\}:\\\{0,1\\\}^\{n\}\\times\\\{0,1\\\}^\{n\}\\to\\\{0,1\\\}^\{m\}such thatExt​\(𝐗,𝐘\)\\mathrm\{Ext\}\(\\mathbf\{X\},\\mathbf\{Y\}\)isε\\varepsilon\-close to the uniform distribution on\{0,1\}m\\\{0,1\\\}^\{m\}in total variation distance\.

Toeplitz construction\.The specific construction used in QSignAI is the Toeplitz\-matrix extractor\. A Toeplitz matrixTThas constant diagonals \(Ti,j=tj−iT\_\{i,j\}=t\_\{j\-i\}\), so anm×nm\\times nmatrix is determined by onlym\+n−1m\+n\-1bits\. In our implementation, these seed bits are drawn from the DM1 source𝐘\\mathbf\{Y\}, while the SV1 source𝐗\\mathbf\{X\}provides the data vector\. The bilinear formT⋅𝐗⊕𝐠T\\cdot\\mathbf\{X\}\\oplus\\mathbf\{g\}\(Eq\.[1](https://arxiv.org/html/2605.27729#S4.E1)\) is computationally efficient—requiring only𝒪​\(n​log⁡n\)\\mathcal\{O\}\(n\\log n\)operations via fast polynomial multiplication—and achieves the information\-theoretic extraction rate of the two\-source model\.

Why this enables the full range\.With the two\-source extractor producingε\\varepsilon\-uniform output bits, we obtain enough entropy to selectqnumq\_\{\\mathrm\{num\}\}uniformly from the full range\[0,1000\]\[0,1000\]\(requiring⌈log2⁡1001⌉=10\\lceil\\log\_\{2\}1001\\rceil=10bits\) and simultaneously derive the 32\-byte nonce𝐫\\mathbf\{r\}\(requiring 256 bits\)\. Without extraction, the raw single\-source output would be limited to one bit per shot and could not reliably produce 266 uniform bits\. The extractor thus*simultaneously*enables \(i\) uniform quantum randomness across the entire required range and \(ii\) replay resistance through the nonce𝐫\\mathbf\{r\}fed into the ToyLWE signature \(Eq\.[3](https://arxiv.org/html/2605.27729#S4.E3)\)\.

### IV\-CCircuit B: 2\-Qubit Bell State Measurement

Circuit B creates the Bell state\|Φ\+⟩=\(\|00⟩\+\|11⟩\)/2\|\\Phi^\{\+\}\\rangle=\(\|00\\rangle\+\|11\\rangle\)/\\sqrt\{2\}\[[3](https://arxiv.org/html/2605.27729#bib.bib6),[2](https://arxiv.org/html/2605.27729#bib.bib7)\]\. Two hundred measurement shots yield the probability distribution𝜷=\[P​\(\|00⟩\),P​\(\|01⟩\),P​\(\|10⟩\),P​\(\|11⟩\)\]\\bm\{\\beta\}=\[P\(\|00\\rangle\),\\,P\(\|01\\rangle\),\\,P\(\|10\\rangle\),\\,P\(\|11\\rangle\)\]\. In an ideal simulator,P​\(\|00⟩\)≈P​\(\|11⟩\)≈0\.5P\(\|00\\rangle\)\\approx P\(\|11\\rangle\)\\approx 0\.5; small deviations from ideal statistics provide a visual encoding of the simulated Bell\-state statistics embedded in every participant’s badge colour\. The circuit parameters for both circuits are summarised in Table[III](https://arxiv.org/html/2605.27729#S4.T3)\.

TABLE III:Quantum Circuit Parameters\.■\\blacksquareCircuit A \(Two\-Source QRNG\);■\\blacksquareCircuit B \(Bell state\)\.TABLE IV:Entropy Budget for the QSignAI Signature PipelineThe quantum\-extracted bits serve primarily as identity visualisation \(qnumq\_\{\\mathrm\{num\}\}determines card hue via Eq\. \(6\)\) and as a perceptual encoding of quantum phenomena, while the cryptographic strength of the signature comes from the 256\-bit extractor\-derived nonce and SHAKE\-256 mixing\.

### IV\-DToyLWE Signature Derivation

The quantum number seeds a ToyLWE\-inspired signature using SHAKE\-256 \(Secure Hash Algorithm 3 extendable\-output function\)\[[15](https://arxiv.org/html/2605.27729#bib.bib25)\]:

𝒮\\displaystyle\\mathscr\{S\}=𝖲𝖧𝖠𝖪𝖤​\-​𝟤𝟧𝟨​\(𝒰​‖qnum‖​r\),\\displaystyle=\\mathsf\{SHAKE\\text\{\-\}256\}\(\\mathscr\{U\}\\,\\\|\\,q\_\{\\mathrm\{num\}\}\\,\\\|\\,r\),\(3\)ℋ\\displaystyle\\mathscr\{H\}=𝖲𝖧𝖠\-𝟤𝟧𝟨\(𝒮\[0:32\]\)\[0:12\],\\displaystyle=\\mathsf\{SHA\\text\{\-\}256\}\(\\mathscr\{S\}\[0\{:\}32\]\)\[0\{:\}12\],\(4\)𝒢\\displaystyle\\mathscr\{G\}=𝖲𝖧𝖠\-𝟤𝟧𝟨\(ℋmsg:ℋent:ℋ\),\\displaystyle=\\mathsf\{SHA\\text\{\-\}256\}\(\\mathscr\{H\}\_\{\\mathrm\{msg\}\}\\,:\\,\\mathscr\{H\}\_\{\\mathrm\{ent\}\}\\,:\\,\\mathscr\{H\}\),\(5\)where𝐫\\mathbf\{r\}is the 32\-byte nonce derived from the Toeplitz extractor output \(Eq\.[2](https://arxiv.org/html/2605.27729#S4.E2)\), ensuring replay resistance,ℋmsg\\mathscr\{H\}\_\{\\mathrm\{msg\}\}is the SHA\-256 hash of the message content, andℋent\\mathscr\{H\}\_\{\\mathrm\{ent\}\}is the SHA\-256 hash ofqnumq\_\{\\mathrm\{num\}\}\. The public key hashℋpk\\mathcal\{H\}\_\{\\mathrm\{pk\}\}\(12 uppercase hex characters\) and 24\-character base64 signature form the badge displayed on each card:𝒬452\|7​𝙱𝟸𝟾𝟺𝙱𝙱𝟹𝙳𝟺𝟷𝟹\\mathcal\{Q\}\_\{452\}\\,\|\\,\\mathtt\{7B284BB3D413\}\. The structural analogy to LWE\[[20](https://arxiv.org/html/2605.27729#bib.bib17)\]is thatqnumq\_\{\\mathrm\{num\}\}plays the role of the secret and SHAKE\-256 mixing plays the role of noise addition\. This is a demonstrative, pedagogical instantiation; the path to production is replacement with CRYSTALS\-Dilithium \(FIPS 204, ML\-DSA\)\[[18](https://arxiv.org/html/2605.27729#bib.bib19)\]\.

### IV\-EVisual Identity Encoding

The quantum number and Bell state probabilities are mapped to a Hue\-Saturation\-Lightness \(HSL\) colour value:

h\\displaystyle h=\(qnum×137\.5\)mod360,\\displaystyle=\(q\_\{\\mathrm\{num\}\}\\times 137\.5\)\\bmod 360,\(6\)s\\displaystyle s=70\+β0×30,\\displaystyle=70\+\\beta\_\{0\}\\times 30,\(7\)l\\displaystyle l=45\+β3×20,\\displaystyle=45\+\\beta\_\{3\}\\times 20,\(8\)whereβ0=P​\(\|00⟩\)\\beta\_\{0\}=P\(\|00\\rangle\)andβ3=P​\(\|11⟩\)\\beta\_\{3\}=P\(\|11\\rangle\)\. The golden\-angle increment \(137\.5°\) maximises perceptual separation between consecutive hue values, a technique from phyllotaxis\. The degree of quantum entanglement is encoded in the vividness and brightness of the card colour: the colour of a card*is*the quantum data, making quantum science perceptually legible without any technical knowledge\.

### IV\-FGraceful Degradation

A 30\-second timeout or Braket task failure triggers a local SHAKE\-256\-seeded fallback\. Affected rows are flagged𝑎𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚:”​𝑇𝑜𝑦𝐿𝑊𝐸​\-​𝑙𝑜𝑐𝑎𝑙​\-​𝑓𝑎𝑙𝑙𝑏𝑎𝑐𝑘​”\\mathit\{algorithm\{:\}\\ "ToyLWE\\text\{\-\}local\\text\{\-\}fallback"\}and𝑑𝑒𝑣𝑖𝑐𝑒:”​𝑙𝑜𝑐𝑎𝑙​\-​𝑓𝑎𝑙𝑙𝑏𝑎𝑐𝑘​”\\mathit\{device\{:\}\\ "local\\text\{\-\}fallback"\}\. The wall never blocks; UX continuity is preserved\. The admin dashboard exposes the𝖽𝖾𝗏𝗂𝖼𝖾\\mathsf\{device\}field per row, making quantum execution provenance auditable\.

### IV\-GEthics and Data Governance

The QSignAI deployment processes real participant data: Telegram usernames, message text, and optional photos\. The following governance measures are in place\.

Consent\.The@​𝗆𝖾𝗇𝗍𝗂𝗈𝗇\\mathsf\{@mention\}acts as a lightweight opt\-in: only messages explicitly tagging the bot are processed\. This follows the Telegram Bot API opt\-in model and is disclosed to participants at event registration\.

Data minimisation\.Only mention\-bearing messages are persisted; all other group traffic is ignored\. Photos are stored in an encrypted S3 bucket with 30\-day automatic expiration\. Username and message text are pseudonymised for analytics\.

Retention and deletion\.Soft\-delete \(𝗁𝗂𝖽𝖽𝖾𝗇=𝗍𝗋𝗎𝖾\\mathsf\{hidden=true\}\) removes data from the public wall immediately\. Hard deletion of all participant data from DynamoDB and S3 is available on request within 30 days, consistent with the right to erasure\.

Moderation\.Event organisers have password\-protected access to soft\-delete inappropriate content\. Bot Privacy Mode is disabled only for the specific group under active event management, not globally\.

IRB\.No human subjects data has been collected to date; all testing has been conducted with bot\-generated synthetic data and organiser self\-testing\. IRB approval must be obtained before any live participant data collection\.

## VUser Experience Walkthrough

The three\-surface interface is shown in Fig\.[3](https://arxiv.org/html/2605.27729#S5.F3)\. The interface follows a three\-surface guided flow\.full screenshots of each surface are provided in Appendix[B](https://arxiv.org/html/2605.27729#A2)\.

INPUTDISPLAYGOVERN★\\bigstarSurface 1 — Messaging PlatformTelegram, 1B\+ MAU⋅\\cdotzero install⊳\\triangleright@mentionas consent gate⊳\\trianglerightTextand/orphoto⊳\\trianglerightWebhookto QSignAI API⊳\\trianglerightBotPrivacy Modedisabled⊳\\trianglerightMulti\-groupbygroupId⊳\\trianglerightSecret tokenvalidates request→\\rightarrowmessage ingested⋅\\cdot◆\\blacklozengeDB write⋅\\cdot★\\bigstarquantum pipeline \(async\)★\\bigstarSurface 2 — Public Photo WallBrowser⋅\\cdot5\-second polling⋅\\cdotlarge\-screen display⊳\\trianglerightReal\-time cardsanimate in⊳\\trianglerightQuantum badge:𝒬452\|7​𝙱𝟸𝟾𝟺​…\\mathcal\{Q\}\_\{452\}\\,\|\\,\\mathtt\{7B284\\ldots\}⊳\\trianglerightCard colour= Bell state⊳\\trianglerightDrag\-and\-drop\(persisted\)⊳\\trianglerightLightboxfor photo zoom⊳\\trianglerightLeaderboard: top contributors⊳\\trianglerightBadge status:generating→\\tocompletedas quantum pipeline finishes asynchronously \(≤\\leq5 s\)→\\rightarroworganiser reviews⋅\\cdot×\\timesmoderation⋅\\cdot✓\\checkmarkprovenance audit◆\\blacklozengeSurface 3 — Admin DashboardPassword\-protected⋅\\cdotbearer token auth⊳\\trianglerightSoft\-delete:hidden=true⊳\\trianglerightQuantum provenanceper row⊳\\trianglerightGroup selector: multi\-venue⊳\\trianglerightdevice: “SV1\+DM1” / “fallback”⊳\\trianglerightalgorithm: ToyLWE\-Braket\-SV1\+DM1⊳\\trianglerightbellState:\[P​\(00\),…,P​\(11\)\]\[P\(00\),\\ldots,P\(11\)\]UX arc:invisible\(quantum circuits run asynchronously\)⟶\\longrightarrowvisible\(Bell state→\\tocard colour\)⟶\\longrightarrowauditable\(provenance per row\)Figure 3:Three\-surface UX architecture of QSignAI\.■\\blacksquareSurface 1: bot\-enabled messaging \(Telegram, 1B\+ MAU\),@​𝗆𝖾𝗇𝗍𝗂𝗈𝗇\\mathsf\{@mention\}as consent gate;■\\blacksquareSurface 2: public photo wall with quantum badge and Bell\-state card colour;■\\blacksquareSurface 3: admin dashboard with per\-row quantum provenance audit\. UX arc:invisible→\\tovisible→\\toauditable\.Surface 1 \(Messaging Platform\):participants use their existing messaging client — zero installation required\. The@​𝗆𝖾𝗇𝗍𝗂𝗈𝗇\\mathsf\{@mention\}acts simultaneously as a routing signal and a lightweight opt\-in consent gate\. Only mention\-bearing messages are persisted\.

Surface 2 \(Public Photo Wall\):the wall polls for new messages every five seconds\. Each card displays the sender’s name, message text, optional photo \(click\-to\-zoom lightbox\), and the quantum badge\. Card colour is derived from Bell state probabilities via \([6](https://arxiv.org/html/2605.27729#S4.E6)\)–\([8](https://arxiv.org/html/2605.27729#S4.E8)\), making quantum entanglement visible to a live audience\. Participants drag cards to preferred positions; positions are persisted as viewport percentages in DynamoDB, surviving window resizes and refreshes\. A leaderboard ranks top contributors by message count\.

Surface 3 \(Admin Dashboard\):event organisers access a password\-protected dashboard exposing per\-row quantum provenance \(𝖽𝖾𝗏𝗂𝖼𝖾\\mathsf\{device\},𝖺𝗅𝗀𝗈𝗋𝗂𝗍𝗁𝗆\\mathsf\{algorithm\},𝗌𝗂𝗀𝗇𝖺𝗍𝗎𝗋𝖾𝖲𝗍𝖺𝗍𝗎𝗌\\mathsf\{signatureStatus\},𝖻𝖾𝗅𝗅𝖲𝗍𝖺𝗍𝖾\\mathsf\{bellState\},𝗊𝗎𝖺𝗇𝗍𝗎𝗆𝖭𝗎𝗆𝖻𝖾𝗋\\mathsf\{quantumNumber\}\)\. Soft\-delete sets𝗁𝗂𝖽𝖽𝖾𝗇=𝗍𝗋𝗎𝖾\\mathsf\{hidden\\ =\\ true\}rather than removing rows, preserving the audit trail and quantum signatures\.

The UX arc moves from*invisible*\(quantum circuits running asynchronously in the background\) to*visible*\(Bell state probabilities encoded as card colour on the public wall\) to*auditable*\(quantum execution provenance inspectable by organisers\)\. This arc is the AI\-for\-Science contribution: quantum phenomena made legible to a live audience through a familiar social interface\.

The complete end\-to\-end interactive flow across all three surfaces is shown in Fig\.[4](https://arxiv.org/html/2605.27729#S5.F4), tracing a single participant’s message from submission through the quantum pipeline to the public wall and admin dashboard\.

★\\bigstarParticipant\(messaging platform\)⋆\\starQSignAI System\(Bot⋅\\cdotCloud⋅\\cdotQuantum\)◆\\blacklozengeOrganiser\(Admin dashboard\)★\\bigstarSurface 1Messaging Platform⋅\\cdotTelegram, 1B\+ MAU1\.@mentionmessage \(text / photo\)⋆\\starBot Layer
2\.Webhook: validates secret token, detects@mention, sanitizes text \(≤\\leq4096 chars\)
3\.\[If photo\] Platform file API→\\todownload→\\toS3 upload
4\.DB write Phase 1:signatureStatus= “generating”→\\tocard on wallimmediatelycard visible \(status: generating\)★\\bigstarQuantum Layer \(async⋅\\cdotnon\-blocking\)
5a\.Circuit A — Two\-source QRNG on SV1 \+ DM1 \(2–8 s\)
SV1 \(ideal\):H→H\\toMeasure→\\to𝐗∈\{0,1\}n\\mathbf\{X\}\\in\\\{0,1\\\}^\{n\}\|\|DM1 \(noisy\):H→H\\toMeasure→\\to𝐘∈\{0,1\}n\\mathbf\{Y\}\\in\\\{0,1\\\}^\{n\}
Toeplitz extractor:𝐙=T⋅𝐗⊕𝐠\\mathbf\{Z\}=T\\cdot\\mathbf\{X\}\\oplus\\mathbf\{g\}
𝐙\\mathbf\{Z\}partitioned intoqnum∈\[0,1000\]q\_\{\\mathrm\{num\}\}\\in\[0,1000\]and 32\-byte nonce𝐫\\mathbf\{r\}
5b\.Circuit B — 2\-qubit Bell State\|Φ\+⟩\|\\Phi^\{\+\}\\rangle\(2–8 s\)
q0:−H−∙−Mq\_\{0\}\{:\}\\ \{\-\}H\{\-\}\\bullet\{\-\}Mq1:−⊕−Mq\_\{1\}\{:\}\\ \{\-\}\\oplus\{\-\}M200 shots→\\to\[P​\(00\),P​\(01\),P​\(10\),P​\(11\)\]\[P\(00\),P\(01\),P\(10\),P\(11\)\]
5c\.ToyLWE derivation:
SHAKE​\-​256​\(𝒰​‖qnum‖​𝐫\)\\mathsf\{SHAKE\\text\{\-\}256\}\(\\mathscr\{U\}\\\|q\_\{\\mathrm\{num\}\}\\\|\\mathbf\{r\}\)→\\toℋpk\\mathcal\{H\}\_\{\\mathrm\{pk\}\}\(12 hex chars\)→\\to𝒮\\mathcal\{S\}\(24\-char base64 signature\)
→\\tohsl​\(h,s%,l%\)\\mathrm\{hsl\}\(h,s\\%,l\\%\): hue fromqnumq\_\{\\mathrm\{num\}\}\(golden angle\), sat/light from Bell state
5d\.DB write Phase 2:signatureStatus= “completed”badge updates \(≤\\leq5 s poll\)★\\bigstarSurface 2: Public Photo WallBrowser polls every 5 s◆\\blacklozengeSurface 3: Admin DashboardPassword\-protected⋅\\cdotprovenance auditmoderation requestAlice Chen@alice\_c
“Hello from ACAIT 2026\!”
★\\bigstar𝒬452\|7​𝙱𝟸𝟾𝟺𝙱𝙱𝟹𝙳𝟺𝟷𝟹\\mathcal\{Q\}\_\{452\}\\,\|\\,\\mathtt\{7B284BB3D413\}
✓\\checkmarkQuantum\-authenticated6\.Card renders with quantum badge
7\.Drag card→\\toposition persists for all viewers
8\.Click photo→\\tofullscreen Lightbox
9\.Leaderboard ranks top contributors10\.Login→\\toBearer token issued
11\.Review message table:
Alice: Q\#452, SV1\+DM1✓\\checkmark
Bob: Q\#731, SV1\+DM1✓\\checkmark
Carol: Q\#89, fallback△\\triangle
12\.Soft\-delete:hidden=true
\(audit trail preserved\)
13\.Provenance per row:
device: “SV1\+DM1”
algorithm: ToyLWE
bellState: \[\.49,\.01,\.01,\.49\]Timing at a GlanceFigure 4:Complete interactive flow of QSignAI across three surfaces\.Actors:★\\bigstarParticipant \(messaging platform user\);⋆\\starQSignAI System \(Bot, Cloud, Quantum\);◆\\blacklozengeOrganiser \(Admin dashboard\)\.Surfaces:■\\blacksquareSurface 1 \(messaging platform, Telegram 1B\+ MAU\);■\\blacksquareSurface 2 \(public photo wall, 5\-second polling\);■\\blacksquareSurface 3 \(admin dashboard, provenance audit\)\. Steps 2–4 complete in<1<1s; quantum tasks \(Steps 5a–5d\) run asynchronously in 4–16 s; the badge updates on the next poll cycle \(≤5\\leq 5s\)\. The fallback path \(30 s Braket timeout\) preserves UX continuity\. See Appendix[C](https://arxiv.org/html/2605.27729#A3)for term definitions\.
## VIDeployment and Discussion

### VI\-AAnswering RQ3: What Successful Deployment Demonstrates

RQ3 asks whether a system combining quantum randomness and AI bot mediation works in practice\. The deployment answers this in three ways\.First, feasibility:the system has been deployed on standard AWS infrastructure, processed real event participants, and operated without blocking on quantum task latency — demonstrating that quantum circuits are compatible with live event timing constraints when the pipeline is designed asynchronously\.Second, bidirectional value:without the quantum circuits, the bot’s identity tokens are indistinguishable from any PRNG\-based system\. Without the bot, the quantum circuits produce outputs that no general audience ever sees\. The combination creates a system with a property neither component achieves alone\.Third, graceful degradation:the fallback mechanism demonstrates that quantum enhancement can be added to an AI system without introducing a single point of failure\.

### VI\-BDeferred Measurable Comparisons

The deployment establishes feasibility qualitatively\. Measurable comparisons deferred to future work include: a rigorous NIST SP 800\-90B\[[16](https://arxiv.org/html/2605.27729#bib.bib9)\]comparison of quantum\-randomness\-seeded versus PRNG\-seeded token distributions; systematic latency benchmarks across SV1, IonQ Aria, Rigetti Ankaa, and IBM Eagle; a controlled user study measuring whether exposure to quantum\-derived visual badges builds quantum literacy; and a formal security comparison of ToyLWE versus CRYSTALS\-Dilithium\[[18](https://arxiv.org/html/2605.27729#bib.bib19)\]under the LWE hardness assumption\[[20](https://arxiv.org/html/2605.27729#bib.bib17)\]\.

### VI\-CSimulator Versus Physical QPU

AWS Braket SV1 is a classical simulation of quantum behaviour — its randomness is ultimately pseudo\-random at the hardware level\. True quantum randomness requires a physical quantum processing unit \(QPU\)\. The architecture is QPU\-ready: replacing the device Amazon Resource Names \(ARNs\) for SV1 and DM1 in the quantum signature module is the only required change\.The 2025 Nobel Prize in Physics\[[14](https://arxiv.org/html/2605.27729#bib.bib12)\]recognised the hardware foundations that make physical QPUs possible; integrating one is the primary near\-term extension\.

### VI\-DAI for Better Life Alignment

QSignAI addresses the conference theme on three axes\.Accessibility:quantum\-authenticated identity is delivered through a channel over one billion people already use\[[8](https://arxiv.org/html/2605.27729#bib.bib29)\]\(addressable platform, not deployment scale\), with no new hardware, no new application, and no technical knowledge required\.Transparency:every participant’s identity has verifiable quantum provenance, auditable by organisers through the admin dashboard\.Scientific literacy:quantum concepts — superposition, entanglement, Bell states — are made perceptually legible to live audiences via visual badges, aligning with Sustainable Development Goal \(SDG\) 4 \(Quality Education\)\[[23](https://arxiv.org/html/2605.27729#bib.bib27)\]\. The system also aligns with SDG 9 \(Industry, Innovation and Infrastructure\), SDG 16 \(Peace, Justice and Strong Institutions\), and SDG 17 \(Partnerships for the Goals\)\.

## VIIFuture Research Ecosystem

★\\bigstarQuantum Computing
⊳\\trianglerightPhysical QPU
\(IonQ, Rigetti, IBM\)
⊳\\trianglerightTrue QRNG \(hardware\)
⊳\\trianglerightNIST SP 800\-90B cert\.
⋆\\starNobel Prize Physics 2025◆\\blacklozengeCryptography
⊳\\trianglerightCRYSTALS\-Dilithium \(FIPS 204\)
⊳\\trianglerightFormal LWE security proof
⊳\\trianglerightPost\-quantum signatures
⋆\\starACM Turing Award 2025∘\\circWeb3 / Blockchain
⊳\\trianglerightTON SBTs \(on\-chain identity\)
⊳\\trianglerightAgentic wallet contracts
⊳\\trianglerightTON Connect integration
non\-transferable credentials★\\bigstarQSignAIdeployed⋆\\starAI Agents / LLM
⊳\\trianglerightMCP server \(@ton/mcp\)
⊳\\trianglerightLLM\-queryable data
⊳\\trianglerightRL moderation agents
natively queryable×\\timesPrivacy & Trust
⊳\\trianglerightCocoon TEE \(hardware\-isolated\)
⊳\\trianglerightZero\-knowledge proofs
⊳\\trianglerightConfidential compute
operator\-blind analytics■\\blacksquareScience Comm\.
⊳\\trianglerightQuantum literacy studies
⊳\\trianglerightLongitudinal evaluation
⊳\\trianglerightCross\-platform standard
public engagement★\\bigstarScience→\\toAIquantum randomnessstrengthens identity⊳\\triangleright1–2 yrQPU, Dilithium,TON, MCP✓\\checkmarkNowQSignAIdeployed⊳\\triangleright2–4 yrAgentic wallets,Cocoon TEE, RL⋆\\starAI→\\toSciencebot makes quantumlegible to audiences⊳\\triangleright4\+ yrZK\-proofs,longitudinal⋆\\star= quantum science enabling AI securityFigure 5:Future research ecosystem with QSignAI as the deployed foundation, arranged as a 3×\\times2 grid of research domains\.■\\blacksquareQuantum Computing\(physical QPU, hardware QRNG\);■\\blacksquareCryptography\(CRYSTALS\-Dilithium, formal LWE proof\);■\\blacksquareWeb3\(TON SBTs, agentic wallets\);■\\blacksquareAI Agents\(MCP server, RL moderation\);■\\blacksquarePrivacy\(Cocoon TEE, ZK\-proofs\);■\\blacksquareSci\. Comm\.\(quantum literacy studies\)\. The timeline strip shows the four\-horizon research roadmap\.QSignAI is the deployed foundation for a broader research ecosystem, illustrated in Fig\.[5](https://arxiv.org/html/2605.27729#S7.F5)\.

Near\-term \(1–2 years\):Physical QPU integration will replace SV1 and DM1 with independent hardware quantum processors \(IonQ Aria, Rigetti Ankaa, IBM Eagle\) for genuine hardware quantum randomness, with NIST SP 800\-90B certification\[[16](https://arxiv.org/html/2605.27729#bib.bib9)\]\. CRYSTALS\-Dilithium replacement\[[18](https://arxiv.org/html/2605.27729#bib.bib19)\]will use the same entropy pipeline with production\-grade PQC signatures and a formal security proof under the LWE hardness assumption\[[20](https://arxiv.org/html/2605.27729#bib.bib17)\]\. TON blockchain on\-chain identity anchoring will issue Soulbound Tokens \(SBTs\) as non\-transferable Non\-Fungible Token \(NFT\) event credentials via TON Connect\. A Model Context Protocol \(MCP\) server\[[1](https://arxiv.org/html/2605.27729#bib.bib28)\]will expose quantum\-authenticated participant data as an MCP resource, natively queryable by Large Language Model \(LLM\) agents\.

Mid\-term \(2–4 years\):Agentic wallet contracts will enable AI\-initiated on\-chain credential issuance\. Cocoon Trusted Execution Environment \(TEE\)\-based confidential compute will support privacy\-preserving analytics over participant data\. Reinforcement Learning \(RL\)\[[21](https://arxiv.org/html/2605.27729#bib.bib16)\]will power adaptive moderation agents\.

Long\-term \(4\+ years\):Zero\-Knowledge \(ZK\) proofs over post\-quantum signatures, longitudinal quantum literacy studies, and cross\-platform quantum identity standards represent the long\-horizon research agenda\.

## VIIIConclusion

QSignAI demonstrates that the relationship between AI and quantum science is bidirectional: quantum randomness, rooted in the principles recognised by the 2025 ACM Turing Award\[[4](https://arxiv.org/html/2605.27729#bib.bib10)\]and enabled by the hardware recognised by the 2025 Nobel Prize in Physics\[[14](https://arxiv.org/html/2605.27729#bib.bib12)\], strengthens AI\-driven identity \(Science for AI, RQ1\); and the AI bot layer, following the template established by AlphaFold2\[[12](https://arxiv.org/html/2605.27729#bib.bib15)\], makes quantum phenomena accessible to general audiences at event scale \(AI for Science, RQ2\)\. Neither direction is theoretical — both are demonstrated in a single production\-deployed system \(RQ3\)\. The central finding: a conversational AI bot and two quantum circuits on cloud simulators are sufficient to make quantum science tangible to over one billion potential users on the Telegram platform today\[[8](https://arxiv.org/html/2605.27729#bib.bib29)\]\.

## Acknowledgment

The authors thank the open\-source community for contributions to the AWS CDK, Next\.js, and AWS Braket SDK ecosystems\. The quantum circuit design draws on the foundational work of Bennett and Brassard\[[4](https://arxiv.org/html/2605.27729#bib.bib10)\]and the experimental verification of Bell inequalities by Aspect et al\.\[[2](https://arxiv.org/html/2605.27729#bib.bib7)\]\.

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## Appendix AUser Experience Screenshots

## Appendix BUser Experience Screenshots

The QSignAI user experience is designed around a three\-surface architecture that makes quantum science tangible to non\-specialist audiences through familiar social interfaces\. The UX arc moves from*invisible*\(quantum circuits running asynchronously in the background\) to*visible*\(Bell\-state statistics encoded as card colour on the public wall\) to*auditable*\(quantum execution provenance inspectable by organisers\)\. Full screenshots of each surface are shown below\.

![Refer to caption](https://arxiv.org/html/2605.27729v2/pics/page-1.png)Figure 6:Surface 1 — Messaging Platform \(Telegram\)\. The participant sends a message with@mentionto the QSignAI bot, which acts as a lightweight opt\-in consent gate\. The bot acknowledges receipt and triggers the quantum pipeline asynchronously\. No technical knowledge or additional software installation is required\. The@mentionsimultaneously serves as a routing signal and a privacy opt\-in, ensuring only intentionally tagged messages enter the system\.##### Surface 1 details\.

As shown in Fig\.[6](https://arxiv.org/html/2605.27729#A2.F6), the participant interacts through their existing Telegram client — zero installation, zero learning curve\. The@mentiontag routes the message to the ECS Fargate backend via HTTPS webhook, where the bot validates the secret token, sanitises text \(4096\-character maximum\), detects the mention entity, and writes an initial record to DynamoDB withsignatureStatus= “generating”\. A card appears on the public wall immediately \(within<1<1s\), even though the quantum pipeline has not yet completed\. If the message includes a photo, the platform file API \(20 MB limit\) downloads it and uploads to an encrypted S3 bucket\.

![Refer to caption](https://arxiv.org/html/2605.27729v2/pics/page-2.png)Figure 7:Surface 2 — Public Photo Wall\. Each card displays the sender’s name, message text, optional photo \(click\-to\-zoom lightbox\), and the quantum badge𝒬𝑛𝑢𝑚\|𝚙𝚔𝚑\\mathcal\{Q\}\_\{\\mathit\{num\}\}\\,\|\\,\\mathtt\{pkh\}\. Card colour is derived from Bell\-state probabilities via Eq\.[6](https://arxiv.org/html/2605.27729#S4.E6)–[8](https://arxiv.org/html/2605.27729#S4.E8), making quantum entanglement visible to a live audience without any technical knowledge\. The wall polls for updates every five seconds\.
##### Surface 2 details\.

Fig\.[7](https://arxiv.org/html/2605.27729#A2.F7)shows the public photo wall, the central*visible*layer of the UX arc\. Each card renders with: \(1\) sender name and Telegram handle; \(2\) message text; \(3\) optional photo thumbnail that opens a fullscreen lightbox on click; \(4\) the quantum badge in the form𝒬𝑛𝑢𝑚\|𝚙𝚔𝚑\\mathcal\{Q\}\_\{\\mathit\{num\}\}\\,\|\\,\\mathtt\{pkh\}where𝑛𝑢𝑚\\mathit\{num\}is the quantum\-randomness\-seeded identity number andpkhis the 12\-character public key hash; and \(5\) a card background colour derived from Bell\-state measurement statistics\. The hue is computed via the golden\-angle increment \(137\.5°\) from Eq\.[6](https://arxiv.org/html/2605.27729#S4.E6)to maximise perceptual separation between consecutive cards\. Saturation and lightness come fromP​\(\|00⟩\)P\(\|00\\rangle\)andP​\(\|11⟩\)P\(\|11\\rangle\)respectively \(Eq\.[7](https://arxiv.org/html/2605.27729#S4.E7)–[8](https://arxiv.org/html/2605.27729#S4.E8)\), so the vividness of the colour*is*the quantum data\. Participants can drag cards to preferred positions; positions are persisted as viewport percentages in DynamoDB, surviving window resizes and refreshes for all viewers\. A leaderboard ranks top contributors by message count\. The browser polls for new messages every five seconds, so the badge updates from “generating” to “completed” asynchronously without any user\-visible blocking\.

![Refer to caption](https://arxiv.org/html/2605.27729v2/pics/page-3.png)Figure 8:Surface 3 — Admin Dashboard\. Event organisers access a password\-protected dashboard exposing per\-row quantum provenance:device\(SV1\+DM1 / fallback\),algorithm,signatureStatus,bellState=\[P​\(\|00⟩\),…,P​\(\|11⟩\)\]=\[P\(\|00\\rangle\),\\ldots,P\(\|11\\rangle\)\], andquantumNumber\. Soft\-delete setshidden=truepreserving the full audit trail\.
##### Surface 3 details\.

Fig\.[8](https://arxiv.org/html/2605.27729#A2.F8)presents the*auditable*layer\. The password\-protected admin dashboard exposes per\-row quantum provenance:device\(“SV1\+DM1” for two\-source extractor execution or “fallback” for local SHAKE\-256\),algorithm\(“ToyLWE\-Braket\-SV1\+DM1” or “ToyLWE\-local\-fallback”\),signatureStatus\(“generating” / “completed”\),bellState=\[P​\(\|00⟩\),P​\(\|01⟩\),P​\(\|10⟩\),P​\(\|11⟩\)\]=\[P\(\|00\\rangle\),P\(\|01\\rangle\),P\(\|10\\rangle\),P\(\|11\\rangle\)\], andquantumNumber\. Soft\-delete setshidden=truerather than removing rows, preserving the audit trail and quantum signatures indefinitely\. Organisers can also soft\-delete inappropriate content\. The dashboard is bearer\-token authenticated and includes a group selector for multi\-venue management\.

## Appendix CGlossary of Key Terms

This appendix defines all key terms, abbreviations, and notation used throughout the paper\. Terms are grouped by domain; inline definitions also appear at first use in the main text\. Quantum circuit notation follows OpenQASM 3\.0; cryptographic notation follows NIST FIPS standards\.

TABLE V:Glossary of Key Terms, Abbreviations, and Notation \(see also inline definitions at first use in the text\)\.TermDefinitionTermDefinitionQRNGQuantum RNG: two\-source extractor over independent quantum measurementsPRNGPseudo\-RNG: deterministic; theoretically reversibleSV1AWS Braket state\-vector simulator \(ideal source\)DM1AWS Braket density\-matrix simulator \(noisy source\)QPUQuantum Processing Unit: physical hardwareToeplitzToeplitz extractor: condenses two weak sources into uniform bitsCNOTControlled\-NOT: 2\-qubit entangling gateBell state\|Φ\+⟩=\(\|00⟩\+\|11⟩\)/2\|\\Phi^\{\+\}\\rangle=\(\|00\\rangle\+\|11\\rangle\)/\\sqrt\{2\}OpenQASMOpen Quantum Assembly LanguageToyLWEToy LWE: demonstrative lattice signatureLWELearning With Errors: lattice hard problemPQCPost\-Quantum CryptographySHAKE\-256SHA\-3 extendable\-output functionHSLHue\-Saturation\-Lightness colour modelqnumq\_\{\\mathrm\{num\}\}Quantum number∈\[0,1000\]\\in\[0,1000\]from Circuit A𝜷\\bm\{\\beta\}Bell state vector from Circuit B𝒮\\mathscr\{S\}Seed:𝖲𝖧𝖠𝖪𝖤​\-​𝟤𝟧𝟨​\(𝒰​‖qnum‖​𝐫\)\\mathsf\{SHAKE\\text\{\-\}256\}\(\\mathscr\{U\}\\\|q\_\{\\mathrm\{num\}\}\\\|\\mathbf\{r\}\)ℋ\\mathscr\{H\}Public key hash: first 12 hex chars𝒢\\mathscr\{G\}Signature: 24\-char base64 string𝒰\\mathscr\{U\}Participant identity string \(username\)AWSAmazon Web Services: cloud platformECSElastic Container ServiceDynamoDBAmazon managed NoSQL databaseS3Amazon Simple Storage ServiceUXUser Experience: three\-surface designMAUMonthly Active UsersMCPModel Context Protocol\[[1](https://arxiv.org/html/2605.27729#bib.bib28)\]LLMLarge Language ModelSBTSoulbound Token: non\-transferable NFTTEETrusted Execution EnvironmentZKZero\-Knowledge proofRLReinforcement Learning\[[21](https://arxiv.org/html/2605.27729#bib.bib16)\]SDGSustainable Development Goal\[[23](https://arxiv.org/html/2605.27729#bib.bib27)\]nonce32\-byte replay\-resistance value from extractor outputTerms grouped by domain\. All circuit notation follows OpenQASM 3\.0\. Inline definitions at first use in the main text\.