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Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models
Aoyu Zhang, Dongping Liu, Luyao Zhang
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 98%
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Summary
Quantum Cinema is an open-source, browser-based interactive application designed to bridge the 'imagination gap' in quantum computing literacy. It uses generative world models (specifically from World Labs) to transform invisible quantum hardware architecturesâtrapped-ion, neutral-atom, and superconducting systemsâinto immersive, navigable 3D cinematic experiences. The platform follows a four-act narrative structure, moving from the history of quantum science (Nobel Prize-winning discoveries) to interactive 3D environments and quantitative comparisons grounded in real AWS Braket hardware metrics. The system is built on a Next.js/React stack and deployed via AWS (CloudFront, ALB, ECS Fargate) using a static-first architecture to ensure high accessibility without specialized hardware.
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Quantum Cinema â builtwith â Next.js
confidence 100% · Quantum Cinema is built as a single-page application (SPA) using Next.js 16
Quantum Cinema â deployedon â Amazon Web Services
confidence 100% · The application is deployed on Amazon Web Services (AWS)
Quantum Cinema â groundedin â AWS Braket
confidence 100% · scientifically grounded in curated metrics from Amazon Web Services (AWS) Braket quantum hardware
Quantum Cinema â uses â World Labs
confidence 100% · All three-dimensional environments are generated using WorldLabs' generative world model platform
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Abstract
Abstract:Quantum computing promises transformative advances across science and industry, yet the physical hardware that enables these computations remains invisible to the public: quantum processors operate inside sealed dilution refrigerators at temperatures near absolute zero, making direct observation impossible. This "imagination gap" between quantum computing's growing societal impact and the public's ability to visualize it represents a significant barrier to quantum literacy and workforce development. We present Quantum Cinema, an open-source, browser-based interactive application that closes this gap by transforming invisible quantum hardware into explorable, cinematic experiences using generative world models. Quantum Cinema guides users through a four-act narrative -- from the foundational Nobel Prize-winning science of quantum entanglement, through curated video introductions to three major quantum computing architectures (trapped-ion, neutral-atom, and superconducting systems), into immersive three-dimensional generative worlds that make invisible quantum phenomena observable, and finally to interactive radar-chart comparisons grounded in real quantum device specifications. All three-dimensional environments are generated using WorldLabs' generative world model platform and are scientifically grounded in curated metrics from Amazon Web Services (AWS) Braket quantum hardware. Quantum Cinema requires no installation, no specialized hardware, and no quantum computing background. It is designed to serve two distinct communities: scholars and developers seeking to replicate or extend the platform, and educators, researchers, and science communicators seeking an intuitive tool for explaining quantum hardware to diverse audiences. This paper describes the system architecture, the generative world model pipeline, use cases for both communities, and directions for future work.
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- Source: https://arxiv.org/abs/2606.17102v1
- Canonical: https://arxiv.org/abs/2606.17102v1
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Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models. Aoyu Zhangâ Dongping Liuâ Luyao Zhangâ * â Authors are listed in alphabetical order by 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 Quantum computing promises transformative advances across science and industry, yet the physical hardware that enables these computations remains invisible to the public: quantum processors operate inside sealed dilution refrigerators at temperatures near absolute zero, making direct observation impossible. This âimagination gapâ between quantum computingâs growing societal impact and the publicâs ability to visualize it represents a significant barrier to quantum literacy and workforce development. We present Quantum Cinema, an open-source, browser-based interactive application that closes this gap by transforming invisible quantum hardware into explorable, cinematic experiences using generative world models. Quantum Cinema guides users through a four-act narrativeâfrom the foundational Nobel Prize-winning science of quantum entanglement, through curated video introductions to three major quantum computing architectures (trapped-ion, neutral-atom, and superconducting systems), into immersive three-dimensional generative worlds that make invisible quantum phenomena observable, and finally to interactive radar-chart comparisons grounded in real quantum device specifications. All three-dimensional environments are generated using World Labsâ generative world model platform and are scientifically grounded in curated metrics from Amazon Web Services (AWS) Braket quantum hardware. Quantum Cinema requires no installation, no specialized hardware, and no quantum computing background. It is designed to serve two distinct communities: scholars and developers seeking to replicate or extend the platform, and educators, researchers, and science communicators seeking an intuitive tool for explaining quantum hardware to diverse audiences. This paper describes the system architecture, the generative world model pipeline, use cases for both communities, and directions for future work. â [-1pt]Ion [-2pt]Trap âł 1 âł 2 âł 3 âł 4 âł 5 â [-1pt]Neutral [-2pt]Atom âł 1 âł 2 âł 3 âł 4 âł 5 â [-1pt]J [-2pt]Chip âł 1 âł 2 âł 3 âł 4 âł 5 Figure 1: The three generative world models of Quantum Cinema, each showing five navigable views. Top: trapped-ion (teal)âytterbium ions in a Paul trap. Middle: neutral-atom (orange)ârubidium array via optical tweezers. Bottom: superconducting (violet)âJosephson-junction chip in a dilution refrigerator. I Introduction Quantum computing stands poised to transform science, industry, and society. From drug discovery and materials science to cryptography and financial modeling, the potential applications of quantum computational advantage span nearly every sector of the global economy [4]. Yet there exists a profound imagination gap: while the software layer of quantum computingâquantum circuits, algorithms, and gatesâhas become increasingly accessible through educational tools and cloud platforms, the hardware itself remains fundamentally invisible to the vast majority of researchers, students, and the public. Quantum processors operate inside massive dilution refrigerators at temperatures measured in millikelvin (thousandths of a degree above absolute zero), sealed within shielded environments that no human eye can penetrate. The physical reality of quantum computing hardwareâthe golden coaxial cables, the superconducting quantum bits (qubits) etched onto silicon chips, the layered cryogenic stages descending toward absolute zeroâhas remained locked behind laboratory walls and abstracted away into circuit diagrams and mathematical notation. The scientific significance of quantum phenomena has received the highest levels of international recognition. The 2022 Nobel Prize in Physics was awarded to Alain Aspect, John Clauser, and Anton Zeilinger for their pioneering experiments establishing quantum entanglementâthe counterintuitive property by which particles become correlated in ways that defy classical explanation [31]. This recognition underscored that quantum mechanics is not merely a theoretical framework but a physical reality with profound technological implications. More recently, the 2025 Nobel Prize in Physics further affirmed the growing centrality of quantum technologies, cementing the fieldâs place at the forefront of 21st-century science [33]. Together, these awards signal a societal moment in which quantum literacy has become essential not only for physicists but for the broader scientific and engineering communities. The intersection of artificial intelligence (AI) and quantum science represents one of the most promising frontiers in modern research. The 2024 Nobel Prize in Physics, awarded to John Hopfield and Geoffrey Hinton for foundational discoveries in neural networks and machine learning [32], underscored the transformative role of AI in scientific discovery. Parallel advances in quantum machine learning (QML) â the use of quantum computers to enhance machine learning algorithms and vice versa â have demonstrated potential advantages in areas ranging from molecular simulation to optimization [4]. However, the inaccessibility of quantum hardware remains a bottleneck: even as AI models grow more capable of reasoning about quantum systems [13], the physical reality of quantum processors remains hidden from the researchers, educators, and students who most need to understand them. This paper bridges that gap by applying generative world models â a technology at the forefront of AI research [35] â to the specific scientific challenge of quantum hardware visualization. For researchers outside quantum physicsâincluding the artificial intelligence (AI) and computer science communities that this venue servesâthis invisibility creates a significant barrier to engagement. Terms such as superconducting qubit, Josephson junction, cryogenic stage, and quantum control electronics remain opaque without a tangible mental model. The physical architecture of a quantum computer, from room-temperature control electronics to the mixing chamber plate at the base of the dilution refrigerator, follows a spatial logic that is difficult to convey through text or two-dimensional diagrams alone. Current visualization approaches fall into two categories, each with significant limitations. Circuit-level simulators such as Quirk [11] and the IBM Quantum Experience [14] provide excellent interactive environments for learning quantum logic gates and circuit construction, but they operate entirely at the abstract level of quantum informationâthe physical hardware that executes these circuits remains unseen. On the other end of the spectrum, virtual reality (VR) approaches such as QuantumEyes [27] and the Quantum Flytrap Virtual Lab [20] have demonstrated immersive quantum environments, but they require specialized VR headsets and significant technical setup, limiting their accessibility for casual exploration and classroom deployment. A survey of quantum games and educational tools [29] confirms that no existing platform bridges the gap between hardware-free accessibility and rich, physically grounded visualization of quantum computing infrastructure. Similarly, prior work in VR-based quantum education [37] has validated the pedagogical value of spatial immersion but has not addressed the challenge of scalable, hardware-agnostic deployment. We present Quantum Cinema, the first interactive cinematic platform that leverages generative world models to make quantum computing accessible through immersive three-dimensional (3D) narrative environments. Generative world modelsâAI systems that synthesize interactive, navigable 3D scenes from visual data or semantic descriptionsâhave emerged as a transformative technology for visual storytelling and education. These models, exemplified by systems capable of generating photorealistic, physically consistent environments from image inputs [36] and from language or image prompts [35], enable a fundamentally new approach to scientific communication: rather than manually constructing 3D assets through traditional computer graphics pipelines, we can generate explorable worlds that are both visually compelling and scientifically informative. The application of generative AI to scientific visualization has been recognized as a frontier with vast untapped potential [3]. Quantum Cinema harnesses this capability to create the first interactive cinematic journey through the landscape of quantum computingâweaving together Nobel Prize-winning science, generative world-model architectures, immersive exploration, and quantitative comparison into a unified four-act narrative. The experience is structured as a four-act narrative. Act I presents an interactive timeline of Nobel Prize laureates in quantum science, enabling viewers to explore the foundational discoveries that shaped the field. Act I showcases generative video worlds for each major quantum computing architectureâsuperconducting circuits, trapped ions, and neutral atomsâdemonstrating how world models can visualize hardware-specific physics. Act I offers an immersive 3D environment generated from World Labs technology, allowing viewers to freely explore a navigable quantum world. Act IV provides a quantitative comparison across architectures through interactive radar charts, supporting evidence-based understanding of trade-offs. This narrative architecture transforms fragmented knowledge into a coherent pedagogical journey, ensuring that viewers acquire conceptual understanding alongside visual appreciation. The four-act structure is described in detail in Section I. This work makes two contributions tailored to the dual audience of this paper. First, for AI researchers and systems developers, we provide a complete technical pipeline demonstrating how generative world models can be adapted for scientific hardware visualization, including our approach to generating physically consistent cryogenic environments, handling domain-specific constraints, and integrating narrative flow with interactive exploration. This pipeline is replicable and extensible to other domains of scientific infrastructure that suffer from similar accessibility barriers. Second, for educators, science communicators, and quantum computing practitioners, we offer an immediately usable cinematic experience that renders quantum hardware tangible for the first time, suitable for classroom instruction, public outreach, and professional training. Data and Code Availability: Quantum Cinema is released under the MIT License. The complete source code, documentation, and generative world templates are available on GitHub111https://github.com/QuantBlockchain/quantum-cinema. A permanent archived version (v1.0.0) has been deposited on Zenodo [24]. The repository includes installation instructions, the full AWS deployment configuration, bilingual documentation, teaching guides for all three architectures, and templates for extending the platform to additional quantum hardware modalities. No proprietary datasets are required: all device parameters are sourced from published manufacturer specifications and the AWS Braket service documentation. The remainder of this paper is organized as follows. Section I surveys related work in quantum visualization, generative world models, and AI-driven scientific communication. Section I details the Quantum Cinema architecture and four-act narrative design. Section VI concludes with limitations and future directions. I Related Work TABLE I: Comparison of Quantum Education and Visualization Platforms Tool Venue â Circuit â Hardware âș Interact. W Web â GenAI âł No Install Quirk [11] Webâ16 â« â â« â« â â« IBM Q Exp. [14] IBMâ24 â« â â« â« â â« QuantumEyes [27] TVCGâ23 â« â â« â â â VENUS [28] EuroVisâ23 â« â â« â â â QNotation [22] QCEâ24 â« â â« â« â â« QWalkVis [16] QCEâ23 â« â â« â« â â« Virtual Lab [20] SPIEâ22 â« â â« â« â â« Intuit [17] CHIâ25 â â â« â â â VR Quantum [37] VRSTâ20 â â â« â â â Black Opal [23] 2024 â« â â« â« â â« Quantum Cinema 2025 â« â« â« â« â« â« Legend. â« = full support; â = partial support; â = not supported. Categories: â Circuit â visualization of quantum circuit diagrams and gate-level operations; â Hardware â rendering of physical quantum processor architectures as spatial environments; âș Interactive â user manipulation and real-time feedback; W Web â browser-based delivery without native application installation; â GenAI â use of generative artificial intelligence (world models, neural rendering) for content creation; âł No Install â immediate accessibility without setup, registration, or specialized hardware. Quantum Cinema is the first platform to offer all six capabilities simultaneously. We position Quantum Cinema at the intersection of four active research areas: quantum computing visualization, immersive quantum education, generative artificial intelligence (AI) for scientific visualization, and AI for quantum science. In what follows, we review the most relevant prior work in each area and identify the key gaps our system addresses. I-A Quantum Computing Visualization Tools The earliest and most widely adopted tools for quantum computing education operate at the circuit level, enabling users to construct and simulate quantum circuits through graphical interfaces. Quirk, developed by Gidney at Google, remains the most popular browser-based quantum circuit simulator, offering drag-and-drop construction, real-time state-vector simulation, and support for up to 16 qubits entirely within the browser [11]. Its accessibility and zero-installation model have made it a staple in undergraduate quantum computing courses. Similarly, the IBM Quantum Experience provides a cloud-based platform with a visual circuit composer, allowing users to execute quantum programs on real superconducting quantum processors [14]. While powerful, these platforms present quantum computation primarily through abstract circuit diagrams, leaving the underlying physical hardware opaque to the learner. Recent research has introduced novel visual encodings to improve circuit interpretability. Ruan et al. proposed QuantumEyes, an interactive visualization system centered on a âdandelion chartâ that maps multi-qubit states to radial visual patterns; their design was validated through 12 expert interviews [27]. In subsequent work, the same authors introduced VENUS, a two-dimensional (2D) geometrical representation of quantum states that generalizes the conventional Bloch sphere to multi-qubit systems [28]. Complementary efforts have focused on pedagogical notation: Norrie et al. developed QNotation, a visual notation translator that bridges formal Dirac notation with intuitive graphical representations for novice learners [22]. For domain-specific education, Jordon et al. created QWalkVis, an interactive visualization tool for quantum walks designed to teach stochastic quantum processes [16]. In the optical domain, Quantum Flytrapâs Virtual Lab offers a no-code, drag-and-drop simulator for optical quantum circuits supporting up to three entangled photons [20]. Despite these advances, all existing circuit-level tools share a common limitation: they visualize quantum computation through abstract symbolic representations rather than rendering the physical hardware itself as an explorable spatial environment. I-B Immersive and Interactive Quantum Education A growing body of work has explored immersive technologies to improve quantum concept comprehension. Zable and Velloso conducted the first controlled study comparing virtual reality (VR) and desktop interfaces for quantum education, using Bloch sphere tutorials to demonstrate that VR can significantly improve spatial understanding of single-qubit states [37]. More recently, Karunathilaka et al. presented Intuit at ACM CHI 2025, an augmented reality (AR) system that explains quantum concepts through everyday analogies projected into the userâs physical environment [17]. Quantum Flytrapâs Virtual Lab also contributes in this space by providing web-based interactive quantum simulation accessible without specialized hardware [20]. Song et al. conducted a systematic review of extended reality (XR) in quantum education, finding that while immersive modalities show promise for conceptual learning, adoption remains limited by hardware cost, setup complexity, and scalability concerns [30]. These findings reveal a critical tension: VR and AR approaches require specialized headsets or equipment that limit accessibility, while fully web-based immersive experiencesâwhich could reach the broadest audienceâremain underexplored in quantum education. I-C Generative AI for Scientific Visualization Generative world models represent a paradigm shift in how complex environments can be synthesized from natural language or structural descriptions. In scientific visualization, this capability enables the automatic generation of explorable three-dimensional (3D) scenes from high-level specifications. Xie et al. introduced PhysGaussian at CVPR 2024, integrating physics simulation with 3D Gaussian splatting to produce dynamic, physically grounded environments; the work has since accumulated over 470 citations [36], underscoring the communityâs interest in generative 3D content. Basole and Major proposed a comprehensive framework for integrating generative AI into scientific visualization pipelines, identifying data-to-scene translation as a key challenge [3]. In industry, World Labsâ Marble platformâfounded by Fei-Fei Liâdemonstrates that generative 3D world models can produce consistent, navigable environments from single images or text prompts [35]. Concurrently, a comprehensive survey by Zhu et al. examined whether video diffusion models such as Sora can function as world simulators, concluding that while limitations remain, these models exhibit emerging capabilities for physical reasoning and environment generation [38]. To our knowledge, generative world models have not yet been applied to quantum hardware visualization, leaving a significant opportunity unexplored. I-D AI for Quantum Science The convergence of AI and quantum science has emerged as a major research direction with applications spanning simulation, optimization, and discovery. Biamonte et al. provided a comprehensive survey of quantum machine learning (QML), establishing the theoretical foundations and identifying near-term opportunities on noisy intermediate-scale quantum (NISQ) devices [4]. On the algorithmic front, variational quantum eigensolvers (VQE) and the quantum approximate optimization algorithm (QAOA) have become flagship approaches for applying quantum computers to real-world problems in chemistry and combinatorial optimization [8]. Complementing these algorithmic advances, Carleo and Troyer demonstrated that neural networks can represent quantum many-body states with remarkable accuracy, introducing the paradigm of neural quantum states for simulating quantum systems that would be intractable for classical methods [7]. While these approaches use AI to advance quantum science, few works use AI to make quantum science accessible to broader audiences. Quantum Cinema occupies this unique position at the intersection of AI-driven visualization and quantum education, applying generative world models to bridge the accessibility gap identified across all three research areas above. Table I summarizes the capabilities of existing tools across eight key dimensions. No prior system simultaneously supports circuit-level accuracy, hardware environment visualization, full interactivity, web-based delivery, generative AI content creation, AI-for-quantum-science framing, and no-code accessibility. To address these gaps, we present Quantum Cinema, a unified platform that combines generative world models with quantum circuit simulation to produce interactive cinematic walkthroughs of quantum computing hardware, accessible from any modern web browser without installation or specialized equipment. I System Design and Architecture This section presents the end-to-end architecture of Quantum Cinema, an interactive web application that combines generative world models with cinematic storytelling to explain quantum computing hardware. We describe the cloud deployment stack (Section I-A), the four-act narrative structure that guides users through the experience (Section I-B), and the three quantum architectures featured in the system (Section I-C). Detailed walkthroughs of each act, including annotated screenshots, are provided in Appendix B. I-A System Architecture Quantum Cinema is built as a single-page application (SPA) using Next.js 16 with React 19, authored entirely in TypeScript [34, 19]. This stack provides server-side rendering, automatic code splitting, and a component-based architecture that supports both cinematic scroll-driven animations and interactive 3D world embedding within a unified codebase. The application is deployed on Amazon Web Services (AWS) [2] following a three-tier cloud architecture optimized for global content delivery and automatic scaling. As illustrated in Fig. 2, user requests first reach an AWS CloudFront Content Delivery Network (CDN) edge location, which serves cached static assets and forwards dynamic requests to an Application Load Balancer (ALB). The ALB distributes traffic across tasks running in AWS Elastic Container Service (ECS) Fargate, a serverless compute engine that eliminates the need to manage underlying virtual machine infrastructure. UCLAWUserBrowserCloudFrontCDNALBECS FargateNext.js 16 SPAWorld Labsmarble.wlabs.aiStatic Assets: Videos + ImagesHTTPSsecretport 3000iframe3D streamNo Database â · No Live QPU â · Static-FirstMissing secret â 403 Forbidden Figure 2: System Architecture of Quantum Cinema. The static-first design requires no database and places no live quantum processing unit (QPU) in the request path. All content is baked into the container at build time; 3D worlds stream from World Labs via public URL embedding. Requests without the shared CDN secret header are rejected at the edge. Each container runs the Next.js standalone build on Node.js 20 Alpine Linux with a non-root user for security hardening. The service auto-scales between one and four tasks based on 70 % CPU utilization, with circuit-breaker rollback to maintain availability during deployment updates. All static assetsâincluding pre-rendered videos, Nobel laureate photographs, and architecture diagramsâare baked directly into the container image at build time. This static-first design eliminates runtime dependencies on object storage, databases, or live quantum cloud services. The immersive 3D environments stream from World Labs [35] via public Universal Resource Locator (URL) embeddings, allowing generative world content to render directly from the providerâs infrastructure without intermediate processing. Table I summarizes the deployment parameters. TABLE I: System Specifications of Quantum Cinema Layer Component Technology âšâ© [-2pt]Frontend Web Framework Next.js 16 / React 19 Language TypeScript â [-2pt]Cloud CDN CloudFront Load Balancer ALB Compute ECS Fargate (1â4 tasks, 70% CPU) ⥠[-2pt]Runtime Container Node.js 20 Alpine (non-root) 3D Platform World Labs (marble.worldlabs.ai) â [-2pt]Unique Database None (static-by-design) QPU in Path None (no live quantum hardware) Note. The static-first architecture eliminates all runtime dependencies on databases, quantum processing units (QPUs), and external APIs. All content is baked into the container image at build time. Icons denote architectural layers: âšâ© Frontend, â Cloud, ⥠Runtime, â Unique (none by design). I-B Four-Act Narrative Design The user experience follows a four-act narrative that mirrors cinematic storytelling conventions while progressively building technical understanding. Each act occupies a distinct section of the scroll-driven SPA, with smooth transitions and consistent visual theming. Fig. 3 depicts the overall flow, and Table I provides the structural breakdown. Appendix B presents a detailed walkthrough of each act, including annotated screenshots and pedagogical rationale. 1Nobel Prizeâ 2025 Laureate profilesThe Quantum TimelineContextWhy2World Modelsâ Ion trap â Atomâ SuperconductingVideo introductionsConceptsWhat3Exploreâ Generative 3D worldsWorld Labs immersionNavigate + discoverExperienceHow4Compareâ Radar charts5 metrics across 3 architecturesUse-case matchingDecide(a) Historical context â (b) Physical concepts â (c) Immersive exploration â (d) Informed selection Figure 3: The Four-Act Narrative Flow of Quantum Cinema. Each act is numbered, color-coded, and annotated with its pedagogical role and key content. Arrows are labeled with the cognitive transition they enable. TABLE I: The Four-Act Narrative Structure of Quantum Cinema Act Name Purpose Component Key Content 1 â Nobel Prize Establish why quantum matters NobelPrizeStep 2025 Nobel Prize in Physics with interactive history timeline of quantum research 2 â World Models Introduce architectures VideoShowcaseStep Curated videos per architecture: ion trap, neutral atom, superconducting 3 â Explore Immersive 3D experience WorldModelStep Generative world (World Labs): navigable 3D environment with scientific annotations 4 â Compare Hardware comparison ComparisonStep Radar charts (5 metrics Ă 3 architectures) + use-case matching Note. Each act is color-coded and icon-tagged to match Figure 3. The narrative follows a âwhy â what â how â whichâ cognitive progression: Act I motivates through the 2025 Nobel Prize and historical context, Act I introduces physical concepts through video, Act I enables embodied learning through immersive 3D exploration, and Act IV supports decision-making through quantitative comparison. The Component column names the React component implementing each act in the source code. Act IâNobel Prize (Section B-A) establishes why quantum computing matters. Users encounter an interactive horizontal timeline centered on the 2025 Nobel Prize in Physics, with historical context connecting the laureatesâ contributions to the broader arc of quantum research. This creates an emotional and historical anchor for the technical content that follows. Act IâWorld Models (Section B-B) introduces the three quantum hardware architectures through curated video content. Users scroll through vertically stacked architecture cards, each containing a short looping video and a concise description of the underlying physical mechanism. At the conclusion of this act, users select one architecture to explore in depthâa choice that parameterizes the remainder of the experience. Act IâExplore (Section B-C) constitutes the immersive centerpiece. Upon selecting an architecture, the user enters a generative three-dimensional (3D) world representing that quantum hardware platform. These environments are AI-generated interactive scenes from World Labs [35], not physics-based simulations. However, each world is grounded in real device parametersâcryostat geometry, vacuum chamber dimensions, laser cooling apparatusâto ensure visual fidelity and educational value. Users can orbit, zoom, and pan within the scene while annotated hotspots explain individual hardware components. Representative views of all three 3D worlds are shown in Figure 1 of the main text. Act IVâCompare (Section B-D) provides an interactive comparison across all three architectures. Users view animated radar charts plotting each platform across five quantitative metrics: coherence time, gate fidelity, connectivity topology, error rate, and qubit count. This act transforms the qualitative impressions gathered during exploration into directly comparable technical data, reinforcing the learning outcomes of the experience. I-C Three Quantum Computing Architectures Quantum Cinema features three leading quantum computing architectures, chosen to represent distinct physical qubit implementations with contrasting engineering trade-offs. Table IV presents the comparative overview, and the generative world models for each are shown in Figure 1. TABLE IV: Comparison of Three Quantum Computing Architectures in Quantum Cinema â Trapped-Ion â Neutral Atoms â Superconducting Metric (IonQ) (QuEra) (Rigetti) Qubits 20â64 256 80 Connectivity All-to-all Nearest + long-range Lattice (limited) 2-Qubit Fidelity 99.8% 99.5% 95â99% Coherence Time ⌠⌠⌠100 ÎŒ Operating Temp. Room (vacuum) Room (vacuum) ⌠15 mK Key Visualized Phenomenon Linear ion chain with Raman laser addressing Programmable atom array via optical tweezers Josephson junction chip in dilution refrigerator Note. Values are representative snapshots from AWS Braket [1] and manufacturer specifications [15, 25, 26]. Shaded cells indicate the best value for each metric. The three architectures are color-coded and icon-tagged consistently with Figure 1 and Table I: â trapped-ion, â neutral-atom, â superconducting. Trapped-ion systems excel in connectivity and fidelity but have fewer qubits; neutral-atom platforms lead in qubit count with flexible geometry; superconducting devices are the most mature but suffer from shorter coherence times and cryogenic cooling requirements. Trapped-Ion quantum computers suspend charged atoms (ions) in electromagnetic fields within an ultra-high vacuum chamber [6]. Lasers tuned to specific wavelengths manipulate individual ions to perform quantum gate operations. Because all ions share a common trapping potential, trapped-ion systems offer native all-to-all connectivity and exhibit long coherence times, often exceeding one second [15]. Neutral Atom systems use focused laser beams (optical tweezers) to arrange neutral atoms in programmable two-dimensional arrays [5]. By exciting atoms to Rydberg states, engineers exploit the Rydberg blockade effectâin which nearby atoms cannot simultaneously be excitedâto implement entangling gates. Neutral atom platforms offer flexible qubit geometries and dynamic reconfiguration [25]. Superconducting quantum processors fabricate electrical circuits containing Josephson junctions and cool them to millikelvin temperatures inside dilution refrigerators [18]. Microwave pulses manipulate the quantum state of each circuit element. Superconducting systems currently lead in raw qubit count and benefit from mature semiconductor fabrication, though they require extreme cryogenic infrastructure and exhibit shorter coherence times [26, 14]. IV Generative World Model Pipeline This section describes the pipeline for creating the 3D immersive environments that form the experiential core of Quantum Cinema. We detail the five-step world creation methodology, discuss the scientific accuracy of generative visualizations, and explain how developers can extend the platform with new quantum architectures. IV-A World Creation Methodology Each 3D world in Quantum Cinema is created through a five-step pipeline that transforms scientific specifications into navigable, photorealistic environments. The pipeline bridges quantum hardware documentation and generative 3D scene synthesis, enabling rapid prototyping of educational environments without manual 3D modeling. â review1âšâ© text2â Labs AI3â curation4âł embed5extractsubmitrenderapproveiteratePublic URL â iframe Figure 4: The five-stage generative world model pipeline in Quantum Cinema. Each architectureâs immersive 3D environment progresses from scientific literature review (Step 1) through structured prompt engineering (Step 2), AI synthesis via World Labs [35] (Step 3), human curation with iterative refinement (Step 4), and frontend integration (Step 5). The feedback loop between Steps 3 and 4 ensures scientific accuracy before publication. Step 1 â Scientific Concept Extraction. For each quantum architecture, we identify key physical phenomena and structural details from peer-reviewed literature and Amazon Web Services (AWS) Braket device specifications [1]. For example, the trapped-ion world is grounded in the physical description of a linear chain of ytterbium ions confined in a Paul trap (an oscillating electromagnetic field configuration that confines charged particles) and addressed by Raman laser beams (lasers tuned to induce stimulated Raman transitions between atomic energy levels, enabling qubit operations). Similarly, the superconducting world captures the visual character of dilution refrigerators housing quantum processors built from Josephson junctions (superconducting devices consisting of two superconducting electrodes separated by a thin insulating barrier, serving as the fundamental qubit element). Step 2 â Prompt Engineering. We craft detailed text prompts that balance scientific accuracy with visual storytelling. Each prompt incorporates three elements: (1) the physical layout of the hardware (e.g., chandelier structure of a superconducting processor, hexagonal lattice of neutral atoms), (2) salient visual features that distinguish the architecture (e.g., gold-plated coaxial lines, vacuum chamber windows), and (3) reference device photographs from published hardware teardowns and manufacturer documentation to ensure structural fidelity. The prompt for the trapped-ion world, for instance, specifies âa linear chain of ytterbium ions suspended in a vacuum chamber, illuminated by intersecting Raman laser beams, with gold-plated electrodes of a Paul trap visible along the axis.â Step 3 â Generative World Synthesis. The engineered prompts are submitted to the World Labs Marble platform [35], a generative 3D world creation system that produces persistent, navigable environments from text and image inputs. Marble synthesizes scenes using Gaussian splatting (a neural rendering technique that represents 3D scenes as collections of 3D Gaussian primitives, enabling real-time photorealistic novel-view synthesis) [36]. The resulting environments are fully explorable via keyboard and mouse, with spatial audio and dynamic lighting, creating an embodied sense of presence within quantum hardware facilities. Step 4 â Curated Refinement. Generated worlds are iteratively refined using the World Labs Chisel editor, an interactive curation tool that allows authors to adjust camera angles, lighting conditions, material properties, and spatial composition. This step ensures that the environments accurately reflect hardware topology â for example, verifying that the superconducting world conveys the vertical âchandelierâ hierarchy of control electronics above the cryostat, or that the neutral-atom world correctly depicts the two-dimensional array of traps created by optical tweezers (focused laser beams that trap and manipulate individual atoms) and the spatial patterns induced by the Rydberg blockade (a phenomenon where excitation of one atom to a Rydberg state shifts the energy levels of nearby atoms, preventing simultaneous excitation within a critical radius). Step 5 â Integration. The refined world is exported as a public URL via the World Labs viewer and embedded directly into the React frontend component. The viewer handles all rendering, navigation, and event propagation, requiring only a single iframe or WebView integration point. This architecture decouples world creation from application development, allowing pedagogical content to be authored independently of the 3D pipeline. IV-B Scientific Accuracy of Generative Worlds It is essential to emphasize that the 3D worlds in Quantum Cinema are generative visualizations â AI-generated scenes conditioned on real quantum hardware parameters, not exact physical simulations. They make otherwise invisible quantum phenomena (decoherence, laser cooling, energy loss during gate operations) observable as visual narrative, but should not be interpreted as precise physical models. Their pedagogical value lies in building accurate mental models of hardware structure and operational principles, rather than in computational fidelity to quantum mechanical equations. To maintain a meaningful connection to real hardware, we curate key device metrics from actual AWS Braket [1] systems for each architecture: qubit count (the number of individually addressable quantum bits available on the device), gate fidelity (the two-qubit gate success rate, quantifying operational accuracy), connectivity topology (ranging from all-to-all connectivity in trapped-ion systems to nearest-neighbor or limited connectivity in superconducting and neutral-atom devices), coherence time (the duration over which qubits maintain their quantum state before environmental interactions cause decoherence), and error rates (aggregate measures of operational infidelity across the device). These metrics are displayed alongside each world to anchor the immersive experience in quantitative reality. TABLE V: Generative World Model Concepts by Architecture Architecture Scientific Concept Key Visual Elements in Generated World â Trapped-Ion (IonQ) Linear chain of Yb+ ions confined in a Paul trap, addressed by intersecting Raman laser beams for quantum gate operations Glowing blue-white ytterbium ions in perfect equilibrium; gold-violet Raman beams entering from multiple angles; faint golden standing-wave field representing shared vibrational mode; dark cylindrical vacuum chamber â Neutral Atoms (QuEra) Programmable two-dimensional array of Rb atoms held by optical tweezers, with Rydberg blockade enabling multi-qubit gates Red optical tweezer beams crisscrossing to form atom grid; soft blue glow of individual rubidium atoms; Rydberg excitation halo around targeted atoms; reconfigurable geometric patterns (triangular, square) â Superconducting (Rigetti) Josephson junction circuits cooled to ⌠15 mK in a dilution refrigerator, controlled by microwave pulses Golden microwave waveguides routing control signals; superconducting processor chip with circuit traces; frost and ice crystals on copper cooling stages; tall cylindrical dilution refrigerator vessel Note. Each row describes the scientific concept grounding the generative prompt and the resulting visual elements in the AI-generated 3D world. Colors are consistent with Table IV and Figure 1. All three environments are synthesized via World Labsâ Gaussian splatting pipeline from combinations of scientific illustrations and reference device photographs (Appendix C). Table V presents representative prompts and the corresponding visual outputs for each quantum architecture, illustrating how physical specifications are translated into generative scene descriptions. IV-C Extensibility for New Architectures Adding a new architecture follows a modular workflow designed to separate pedagogical content from application logic. First, the developer reviews the scientific literature for the target hardware platform and extracts key physical phenomena, structural parameters, and visual features that distinguish the architecture. Second, these specifications are translated into structured text prompts for the World Labs Marble generative world model platform, following the prompt engineering methodology described in Section IV-A. Third, the generated world is iteratively refined through manual curation to ensure scientific accuracy and pedagogical clarity. Fourth, the developer authors a teaching guide specifying learning objectives, key concepts, discussion questions, and cross-references to existing architectures. Finally, the new world and its teaching content are registered in the application configuration, and the platform is redeployed through its continuous delivery pipeline. This separation of concernsâcontent, world assets, and application logicâensures that domain experts can contribute new quantum hardware visualizations without modifying core application code, a design decision that supports community-driven expansion to emerging architectures such as photonic and topological qubit systems. This modular structure ensures that domain experts can contribute new worlds without modifying core application code. The separation of pedagogical content (Markdown files), 3D world assets (World Labs URLs), and application logic (React components) follows established software engineering principles and lowers the barrier to community contributions. As new modalities such as photonic quantum computing and topological qubits mature [1], they can be incorporated into the platform through this same standardized workflow. V Use Cases and Demonstration This section presents four use cases illustrating how Quantum Cinema serves its dual target communities: educators, researchers, and science communicators seeking an intuitive tool for explaining quantum hardware, and developers seeking to replicate or extend the platform. V-A Use Case 1: Teaching Quantum Entanglement Consider an undergraduate physics instructor preparing a lesson on quantum entanglement for a classroom of students with no prior exposure to quantum computing hardware. The instructor directs students to Quantum Cinema, where each student progresses through the four-act narrative at their own pace. In Act 1âNobel Prize, students encounter the 2022 Nobel Prize in Physics awarded to Alain Aspect, John Clauser, and Anton Zeilinger for their experimental demonstrations of quantum entanglement [31]. The interactive timeline presents each laureateâs portrait, biography, and citation, establishing the historical significance and physical reality of entanglement as an experimentally verified phenomenon. In Act 2âWorld Models, the student selects the trapped-ion architecture card. A short looping video introduces the core physical concept: individual charged atoms suspended in an electromagnetic trap and manipulated by laser beams. The student learns that trapped-ion systems are one of the leading platforms for realizing entangled quantum states in a controlled, repeatable manner [15]. Act 3âExplore delivers the immersive centerpiece. The student enters a generative three-dimensional world depicting a linear chain of trapped ytterbium ions suspended in an ultra-high vacuum chamber. Gold-violet Raman laser beams enter from multiple directions, and a faint golden standing-wave structure represents the collective phonon modeâthe shared vibrational motion of the entire chain that serves as the quantum bus coupling distant qubits. Two highlighted ions at opposite ends of the chain are phase-locked to this shared field. An annotation delivers the key teaching moment: these ions are entangled not through any physical wire, but through their shared coupling to the collective motion of the ion chain. This makes abstract textbook descriptions of entanglement concrete and observable. Teaching guides with discussion questions and conceptual checkpoints accompany the scene [22]. In Act 4âCompare, the student observes that trapped-ion systems offer all-to-all connectivityâany qubit interacts directly with any otherâin contrast to the limited nearest-neighbor connectivity of superconducting architectures or the geometry-constrained connectivity of neutral-atom systems. This observation reinforces why trapped-ion platforms have been central to entanglement research: their native connectivity mirrors the non-local correlations that entanglement produces. V-B Use Case 2: Architecture Comparison for Quantum Researchers A quantum computing researcher evaluating hardware platforms for a specific algorithmic workload can access Act 4 directly, bypassing the narrative Acts 1â3. The interactive radar chart comparison, shown in Fig. 5, plots all three architectures across five quantitative metrics derived from current AWS Braket device specifications [1]: coherence time, two-qubit gate fidelity, qubit count, connectivity, and error rate. The researcher seeks to run two representative algorithms: Shorâs algorithm (a quantum algorithm for integer factorization offering exponential speedup over classical methods) and Quantum Approximate Optimization Algorithm (QAOA) (a variational quantum algorithm for combinatorial optimization by preparing approximate ground states of problem Hamiltonians). For Shorâs algorithm, the critical requirement is long coherence timeâdeep circuits require qubits to maintain their quantum state through many sequential operations. The radar chart reveals that trapped-ion systems, with coherence times exceeding one second [15], and neutral-atom systems [25] are the most suitable candidates. For QAOA, the dominant requirement is large qubit count, as problem scale grows directly with available qubits. Here, neutral-atom platforms lead, followed by superconducting processors with their mature fabrication pipelines [14]. This capability transforms hardware selection from a literature-review exercise into an interactive, visually grounded decision process. Researchers can adjust metric weights, export comparison data, and share configurations via Universal Resource Locator (URL), making Quantum Cinema a practical tool for research group meetings and collaborative hardware evaluation. 12345 Coherence [-1pt](T2 time) Fidelity [-1pt](2-qubit gate) Connectivity [-1pt](topology) Qubit Countâ [-1pt](physical qubits) Error Rateâ [-1pt](per gate) Architectureâ Ion Trapâ Neutral Atomsâ SuperconductingâInverted axes: higher = better.Data: IonQ, QuEra, Rigetti via AWS Braket. Figure 5: Radar chart comparing three quantum architectures across five key metrics. Trapped-ion systems excel in coherence, fidelity, and connectivity; neutral atoms lead in qubit count; superconducting devices offer fast gate speeds. Values normalized 0â5; Error Rate and Qubit Count inverted (higher = better) [1, 6, 5, 18]. V-C Use Case 3: Science Communication and Public Engagement A science journalist preparing an article on the competitive landscape of quantum computing needs to understand the differences between hardware architectures but lacks a physics background. Existing technical documentation assumes familiarity with concepts such as cryogenic cooling, electromagnetic confinement, and laser addressingâbarriers that prevent accurate reporting. Quantum Cinema addresses this gap through its four-act narrative structure, which requires no quantum computing background and progressively builds understanding through visual metaphors. The freezing temperature required for superconducting qubitsâapproximately 15 millikelvin, colder than outer spaceâis rendered as shimmering ice crystals descending through the cryogenic stages of the dilution refrigerator. The laser beams that control trapped-ion qubits appear as golden threads of light, making visible the invisible electromagnetic fields that perform quantum gate operations. The optical tweezers that arrange neutral atoms are depicted as delicate pink beams sculpting a programmable lattice, conveying the programmable reconfigurability of this architecture. These visual metaphors provide accurate mental models that journalists can translate into prose for general readerships, while making quantum hardware tangible and memorable for the public. The entire experience is shareable via a single URL and embeddable into web articles as an iframe, eliminating the installation and hardware barriers that limit virtual-reality-based approaches [37]. Prior work in augmented-reality scientific communication [17] has demonstrated that embedding interactive 3D content within familiar web contexts significantly increases engagement and knowledge retention relative to static diagrams. Quantum Cinema extends this principle to quantum computing, a domain where the physical reality of the technology is inherently inaccessible to direct observation. V-D D. Use Case 4: Extending the Platform For developers and systems researchers who wish to replicate Quantum Cinema or adapt its pipeline to other domains of scientific infrastructure, the platform provides a complete, documented path from source code to deployed application. The replication workflow is designed to require minimal configuration: the application runs locally with a single command after dependency installation, and all static assets are bundled at build time, eliminating external service dependencies during development. The extension workflow for adding new quantum architectures follows the modular pipeline described in Section IV-A. Developers begin by conducting a scientific literature review for the target hardware, extract key physical phenomena and structural features, engineer structured text prompts for the generative world model platform, curate the resulting environment for accuracy, author pedagogical content, and register the new world in the application configuration. This separation of pedagogical content, three-dimensional world assets, and application logic ensures that domain experts can contribute without modifying core code. The deployment pipeline provisions the CloudFront content delivery network, Application Load Balancer, and Elastic Container Service Fargate cluster through infrastructure-as-code configuration. The static-first architecture ensures predictable scaling behavior and low operational overhead, making the platform suitable for classroom deployment, public outreach events, and integration into institutional learning management systems. VI Conclusion, Limitations, and Future Work Quantum Cinema represents the first interactive system to leverage generative world modelsâneural networks that learn to simulate virtual environmentsâfor the visualization of quantum computing hardware, directly addressing the âimagination gapâ between quantum computingâs transformative potential and public understanding. By rendering the invisible subatomic machinery of quantum processors as explorable cinematic worlds, we bridge a critical communication barrier that has long impeded the broader adoption and comprehension of quantum technologies. The platformâs four-act cinematic structureâspanning from a Nobel Prize historical narrative through curated video introductions for conceptual grounding, into freeform 3D exploration, and culminating in side-by-side hardware comparisonâmakes quantum hardware accessible to non-expert audiences while preserving the scientific depth required by researchers. Each of the three featured architecturesâtrapped-ion, neutral-atom, and superconducting quantum processorsâis populated with real device parameters sourced from Amazon Web Services (AWS) Braket, ensuring that every visualization remains grounded in empirically validated hardware specifications. The complete system is open-source, runs entirely in the browser without installation, and is freely accessible to a global audience regardless of technical background or computational resources. We acknowledge several limitations of the current system and outline corresponding future directions across three areas. Fidelity and Coverage The generative worlds are visualizations grounded in real device parameters, not physical simulations; while they build accurate mental models of hardware structure and operational principles, users seeking computational fidelity should consult dedicated quantum simulation frameworks. Device parameters further represent static snapshots rather than real-time data, and the platform is currently limited to three architectures (superconducting, trapped-ion, and neutral atom), with photonic and topological qubit systems under active development. The reliance on a commercial generative platform (World Labs) also introduces a dependency, which we mitigate by documenting our complete prompt engineering methodology so that worlds can be regenerated using alternative platforms as the ecosystem evolves. Future work will pursue live integration with AWS Braket to dynamically update hardware parameters, incorporate additional architectures including photonic and topological qubits, and maintain platform-agnostic documentation for reproducibility. Interactivity and Pedagogy The platform does not currently support interactive quantum circuit execution within the 3D environments, limiting users to observational rather than experimental exploration. Looking ahead, embedding circuit simulators directly within the immersive worlds would allow users to observe how program-level operations manifest physically on each hardware platform. Formal user studies with quantum educators and students are also needed to rigorously evaluate pedagogical efficacy, and integration with established quantum computing curricula and frameworks such as Qiskit, Cirq, and PennyLane would enable seamless adoption within existing educational ecosystems. 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Qiu, X. Li, Y. Feng, Y. Yang, and C. Jiang (2024) PhysGaussian: physics-integrated 3d gaussians for generative dynamics. In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), p. 4389â4398. Cited by: TABLE VII, §I, §I-C, §IV-A. [37] A. Zable, L. C. L. Hollenberg, E. Velloso, and J. Goncalves (2020-11) Investigating immersive virtual reality as an educational tool for quantum computing. In Proc. ACM Symp. Virtual Reality Software and Technology (VRST), p. Article 6, 11 pages. External Links: Document Cited by: §I, §I-B, TABLE I, §V-C. [38] Z. Zhu, X. Wang, W. Liu, et al. (2024) Is Sora a world simulator? a comprehensive survey on general world models and beyond. arXiv preprint arXiv:2405.03520. Cited by: §I-C. Appendix A Glossary Quantum Cinema brings together concepts from four distinct knowledge domains: quantum computing hardware, generative artificial intelligence, web application infrastructure, and foundational quantum science. To serve the interdisciplinary readership of this paperâspanning computer scientists, quantum physicists, educators, and science communicatorsâwe provide below a comprehensive glossary organized by domain. Each table is visually distinguished by a unique color and icon to facilitate quick navigation. Table VI (â teal) defines the physical vocabulary of quantum computing: the three hardware architectures featured in Quantum Cinema (trapped-ion, neutral-atom, and superconducting), their constituent components (Josephson junctions, optical tweezers, Paul traps), and the fundamental quantum mechanical phenomena (entanglement, superposition, decoherence) that make quantum computation possible. These definitions directly inform the generative world models of Act I, ensuring that every 3D environment is grounded in empirically validated hardware descriptions. Table VII (â purple) covers the generative AI technologies that enable Quantum Cinemaâs immersive visualizations: the world model pipeline that transforms scientific specifications into explorable 3D environments, the Gaussian splatting technique used for real-time neural rendering, and the World Labs platform that powers the scene synthesis. These terms bridge the hardware definitions of Table VI with the navigable 3D experiences presented to the user. Table VIII (â navy) documents the web engineering stack: the serverless cloud architecture (AWS ECS Fargate, CloudFront CDN), the front-end framework (Next.js, React, TypeScript), and the single-page application model that together enable Quantum Cinemaâs zero-installation, globally accessible delivery. Understanding this infrastructure is essential for researchers and developers seeking to replicate or extend the platform. Table IX (â amber) situates the work within its scientific context: the Nobel Prizes that motivate the fieldâs importance (Acts IâI), the formal definition of quantum computing, and the canonical algorithms (Shorâs, QAOA, VQE) that inform the hardware comparisons of Act IV. These entries connect the technical content of the paper to the broader scientific narrative that Quantum Cinema presents to its users. TABLE VI: Glossary of Technical Terms: Quantum Computing Hardware â Quantum Computing Hardware Term Definition Ion Trap A quantum computing platform that confines charged atomic ions in electromagnetic fields within an ultra-high vacuum chamber, using precisely tuned laser pulses to perform quantum gate operations on individual ions with high fidelity [[6]]. Neutral Atom An atom with no net electrical charge that is confined and manipulated using focused laser beams called optical tweezers, forming the basis of a quantum computing platform that offers programmable two-dimensional geometries and flexible connectivity patterns [[5]]. Superconducting Qubit A quantum bit implemented using superconducting electrical circuits, typically containing one or more Josephson junctions, that are cooled to millikelvin temperatures to preserve quantum coherence and enable gate operations [[18]]. Josephson Junction A superconducting device consisting of two superconducting electrodes separated by a thin insulating barrier, serving as the fundamental nonlinear circuit element that enables superconducting qubit operation through the Josephson effect [[18]]. Paul Trap An ion trap design that uses oscillating radio-frequency electromagnetic fields to confine charged particles in three-dimensional space without the need for physical walls or containers, named after Wolfgang Paul who shared the 1989 Nobel Prize in Physics for its invention [[6]]. Optical Tweezer A tightly focused laser beam that creates a trapping potential capable of holding and manipulating microscopic particles, used extensively in neutral-atom quantum computing to arrange individual atoms in programmable two-dimensional arrays [[5]]. Rydberg Blockade A quantum phenomenon in which the excitation of one atom to a highly excited Rydberg state shifts the energy levels of nearby atoms within a critical radius, preventing their simultaneous excitation and thereby enabling controlled entangling operations [[5]]. Raman Laser A laser tuned to stimulate Raman transitions between atomic energy levels, a technique widely used in trapped-ion quantum computing to implement both single-qubit rotations and multi-qubit entangling gate operations [[6]]. Quantum Bit (Qubit) The fundamental unit of quantum information that can exist in a superposition of basis states, enabling quantum parallelism and computational advantages over classical binary computation for certain problem classes [[21]]. Coherence Time The characteristic duration during which a quantum system maintains its superposition state before environmental interactions cause decoherence, representing a fundamental limit on the length of quantum computations that can be performed reliably [[4]]. Decoherence The irreversible loss of quantum mechanical properties, including superposition and entanglement, that occurs when a quantum system interacts with its surrounding environment; decoherence represents the primary obstacle to scaling quantum computers [[21]]. Entanglement A quantum mechanical phenomenon in which two or more particles become correlated such that the quantum state of each particle cannot be described independently of the others, regardless of the spatial separation between them; the 2022 Nobel Prize in Physics was awarded for experimental demonstrations of this phenomenon [[31]]. Fidelity A quantitative measure of the accuracy with which a quantum gate operation or quantum state preparation is performed, defined mathematically as the overlap between the intended and actual quantum states, expressed as a value between zero and unity [[8]]. Error Rate The probability that a quantum gate operation produces an incorrect output, typically quantified through randomized benchmarking protocols and reported as an aggregate figure of merit for comparing quantum device performance [[8]]. Gate A quantum logic operation that manipulates one or more qubits through unitary transformations, analogous to classical logic gates but operating on quantum amplitudes rather than binary values [[21]]. Bloch Sphere A geometric representation of a single qubitâs quantum state as a point on the surface of a unit sphere, providing an intuitive visualization of superposition, quantum phase, and the effects of single-qubit rotations [[21]]. Superposition A fundamental quantum mechanical principle stating that a quantum system can exist in multiple states simultaneously until a measurement is performed, which collapses the system into a single definite state [[21]]. Millikelvin (mK) A unit of temperature equal to one thousandth of a kelvin, representing the operating temperature regime for superconducting qubits where thermal noise is suppressed below the energy scale of the quantum computational states [[18]]. Dilution Refrigerator A specialized cryogenic cooling system that uses a mixture of helium-3 and helium-4 isotopes to reach temperatures in the millikelvin rangeâapproximately fifteen thousandths of a degree above absolute zeroâwhich is required for operating superconducting quantum processors [[18]]. Note. These eighteen terms constitute the physical vocabulary of quantum computing as presented in Quantum Cinema. Each definition grounds the corresponding generative world modelâthe immersive 3D environments of Act Iâin empirically validated hardware descriptions drawn from peer-reviewed reviews of trapped-ion [[6]], neutral-atom [[5]], and superconducting [[18]] architectures. Readers seeking a comprehensive introduction to quantum computation may consult Nielsen and Chuang [[21]]. TABLE VII: Glossary of Technical Terms: Generative AI and Scientific Visualization â Generative AI and Scientific Visualization Term Definition Generative World Model An artificial intelligence system that learns to synthesize realistic, interactive virtual environments by predicting the spatial structure, physical dynamics, and visual appearance of scenes from high-level descriptions or partial observations [[10]]. Gaussian Splatting A neural rendering technique that represents three-dimensional scenes as collections of three-dimensional Gaussian primitives, enabling real-time photorealistic novel-view synthesis from sparse input photographs or text descriptions [[36]]. World Labs A company founded by Fei-Fei Li that develops generative artificial intelligence systems for creating persistent, explorable three-dimensional virtual environments from text descriptions and images, providing the platform that powers Quantum Cinemaâs immersive scenes [[35]]. Note. These three terms describe the AI substrate of Quantum Cinema. The generative world model pipeline (Section IV) translates the hardware concepts of Table VI into navigable 3D environments, bridging the âimagination gapâ between abstract quantum physics and public understanding. For a comprehensive survey of world models, see Ding et al. [[10]]. TABLE VIII: Glossary of Technical Terms: Web Application Infrastructure â Web Application Infrastructure Term Definition Amazon Web Services (AWS) Braket A fully managed quantum computing service provided by Amazon Web Services that offers access to quantum hardware from multiple vendors, including trapped-ion, neutral-atom, and superconducting quantum processors, along with classical simulation tools and quantum algorithm development environments [[1]]. Content Delivery Network (CDN) A geographically distributed system of proxy servers that caches and delivers web content to end users from the nearest edge location, thereby reducing latency, improving load times, and enhancing global availability [[2]]. Elastic Container Service (ECS) Fargate A serverless compute engine provided by Amazon Web Services for running containerized applications without requiring the user to provision or manage underlying server infrastructure, enabling automatic scaling and fault-tolerant deployment [[2]]. Next.js An open-source React framework that provides server-side rendering, automatic code splitting, and hybrid static site generation, designed for building production-grade web applications with optimized performance [[34]]. React An open-source JavaScript library for building user interfaces through a component-based architecture that enables declarative, efficient, and flexible front-end development, maintained by Meta Platforms [[19]]. Single-Page Application (SPA) A web application architecture that dynamically updates content through JavaScript without loading entire new pages from the server, providing a fluid, responsive user experience similar to native desktop applications [[34]]. TypeScript A typed superset of JavaScript developed by Microsoft that adds static type checking and advanced language features, improving developer productivity and code reliability for large-scale web applications [[34]]. Note. These seven terms describe the software engineering stack that enables Quantum Cinemaâs zero-installation, globally accessible delivery model (Section I). The static-first, serverless architecture was chosen specifically to eliminate barriers to adoptionâno quantum hardware access, no software installation, and no user account are required. TABLE IX: Glossary of Technical Terms: Foundational Science and Algorithms â Foundational Science and Algorithms Term Definition Nobel Prize in Physics 2022 Awarded to Alain Aspect, John Clauser, and Anton Zeilinger for experiments with entangled photons that established the violation of Bell inequalities and pioneered the field of quantum information science [[31]]. Nobel Prize in Physics 2024 Awarded to John Hopfield and Geoffrey Hinton for foundational discoveries and inventions that enable machine learning with artificial neural networks, underscoring the transformative role of artificial intelligence in scientific discovery [[32]]. Nobel Prize in Physics 2025 Recognized advances at the intersection of quantum science and quantum computing, cementing the fieldâs position at the forefront of modern physics and highlighting the growing societal importance of quantum technologies [[33]]. Quantum Computing A paradigm of computation that exploits quantum mechanical phenomenaâsuperposition, entanglement, and quantum interferenceâto process information in ways that can provide exponential speedups over classical computers for specific tasks [[21]]. Shorâs Algorithm A quantum algorithm for integer factorization that runs in polynomial time, offering an exponential speedup over the best known classical algorithms and demonstrating the transformative potential of quantum computing for cryptography [[21]]. Quantum Approximate Optimization Algorithm (QAOA) A variational quantum algorithm designed for combinatorial optimization problems, which prepares approximate ground states of problem Hamiltonians by alternating between application of a phase separator and a mixer operator [[8]]. Variational Quantum Eigensolver (VQE) A hybrid quantum-classical algorithm that uses a quantum computer to prepare trial states and a classical optimizer to adjust parameters, finding approximate ground state energies of molecular Hamiltonians [[8]]. Note. These seven entries establish the scientific context and algorithmic repertoire of Quantum Cinema. The Nobel Prizes (Act I of Section I) motivate the fieldâs significance, while the algorithms inform the hardware comparison in Act IVâfor instance, Shorâs algorithm demands high coherence (favoring trapped-ion platforms), whereas QAOA requires large qubit counts (favoring neutral atoms [[12]]). Appendix B The Four Acts of Quantum Cinema This appendix provides a detailed walkthrough of each act in Quantum Cinemaâs narrative, with annotated screenshots for Acts I, I, and IV. Act I (the immersive 3D world exploration) is illustrated in Figure 1 of the main text. B-A Act I: Nobel Prize â Establishing Historical Context Act I grounds the user in the historical and scientific significance of quantum mechanics through an interactive timeline of Nobel Prize laureates (Figure 6). The screen presents three Nobel Prizes in Physics: the 2022 award to Aspect, Clauser, and Zeilinger for experimental entanglement; the 2024 award to Hopfield and Hinton for foundational machine learning; and the 2025 award recognizing quantum computing advances. Each laureate entry includes a portrait, citation text, and a one-sentence explanation of their contributionâs relevance to quantum technology. Users scroll through the timeline at their own pace, building the âwhyââthe motivational foundation that answers why quantum computing matters. Figure 6: Act I: Nobel Prize timeline. Users interact with laureate profiles to understand the historical significance of quantum entanglement, neural networks, and quantum computing advances. Pedagogical rationale. Research in science communication emphasizes that historical narrative increases engagement and retention when introducing complex technical topics [9]. By beginning with Nobel Prize laureates rather than abstract physics, Quantum Cinema leverages the authority and familiarity of these awards to build trust and curiosity in non-expert users. B-B Act I: World Models â Introducing Architectures Act I transitions from historical context to technical content through curated video introductions for each of the three quantum architectures (Figure 7). The screen presents a horizontal selector: trapped-ion (teal), neutral-atom (orange), and superconducting (violet). Selecting an architecture plays a short video that visually introduces its key physical featuresâlinear ion chains, optical tweezer arrays, or Josephson junction circuitsâwithout requiring prior quantum physics knowledge. Users may watch all three videos in any order before proceeding. Figure 7: Act I: World Models video showcase. Users select an architecture to watch its introductory video, building conceptual understanding before entering the immersive 3D environment. Pedagogical rationale. The video-first approach follows the âpre-trainingâ principle from multimedia learning theory: exposing learners to key terminology and visual concepts before immersive exploration reduces cognitive load and improves subsequent sense-making [9]. B-C Act I: Explore â Immersive 3D World Exploration Act I is the centerpiece of Quantum Cinema. After selecting an architecture in Act I, the user enters a full-screen, navigable 3D world generated by World Labsâ Gaussian splatting pipeline. Figure 1 of the main text shows fifteen representative views across all three architectures. Trapped-Ion World (teal). Users explore a linear chain of ytterbium ions confined in a Paul trap. Gold-violet Raman laser beams enter from multiple directions. A faint golden standing-wave field represents the shared vibrational mode. Two highlighted ions demonstrate entanglement through the shared mediumâthey are phase-locked not through a physical wire but through collective motion of the ion chain. Neutral-Atom World (orange). Users navigate a two-dimensional array of rubidium atoms held by red optical tweezers. A Rydberg excitation glow surrounds targeted atoms. The programmable geometryâatoms arranged in triangular, square, or arbitrary patternsâis visible and manipulable. Superconducting World (violet). Users explore a superconducting processor chip mounted at the base of a dilution refrigerator. Golden microwave waveguides route control signals. Frost and ice crystals on copper stages visualize the cryogenic environment. Circuit traces show the Josephson junction patterns. Pedagogical rationale. Immersive 3D environments support spatial cognition and embodied learning in ways that static diagrams cannot [10]. The generative world model approach makes invisible quantum phenomenaâdecoherence, laser cooling, energy lossâobservable as visual narrative, directly addressing the âimagination gapâ described in Section I. B-D Act IV: Compare â Quantitative Architecture Selection Act IV provides the synthesis: an interactive comparison dashboard where users evaluate all three architectures across five metrics (Figure 8). The screen displays a radar chart comparing coherence time, gate fidelity, connectivity, error rate, and qubit count, alongside a use-case matching panel that suggests optimal architectures for specific algorithms. For example, Shorâs algorithm (requiring high coherence) is matched to trapped-ion platforms, while QAOA (requiring many qubits) is matched to neutral atoms. Figure 8: Act IV: Architecture comparison dashboard. The radar chart compares three architectures across five metrics, with use-case matching recommendations. Pedagogical rationale. The comparison stage implements the âsynthesisâ level of Bloomâs taxonomy: users must integrate knowledge from all previous acts to make an informed decision. The quantitative grounding in real AWS Braket device parameters ensures that the comparison has scientific validity, while the visual radar chart format makes multidimensional data accessible to non-experts. Appendix C Generative World Model Details This appendix details the generative world model creation process for each of the three quantum computing architectures in Quantum Cinema. For each architecture, we present: (i) the scientific concepts and reference device photographs that inform the prompt, (i) the AI-generated immersive world output, and (i) five representative navigable views (Figure 1 of the main text). The generation pipeline follows the five-step process described in Section IV and illustrated in Figure 4. C-A Trapped-Ion World Model Scientific basis. Trapped-ion quantum computers confine charged atoms (ions) in electromagnetic fields within an ultra-high vacuum chamber [6]. Individual ions are addressed by precisely tuned laser beams to perform quantum gate operations. The key visualized phenomena include: the linear ion chain suspended in the trap, intersecting Raman laser beams, and the shared vibrational mode that mediates entanglement between ions. Input. The generative prompt combines a scientific concept illustration of ionization (the process of creating charged ions from neutral atoms), a reference photograph of an IonQ trapped-ion device, and an original reference image of the ion trap apparatus (Figure 9). These inputs establish the structural fidelity and physical accuracy of the generated scene. (a) Concept: ionization process (b) Device: IonQ trapped-ion system (c) Original: ion trap apparatus Figure 9: Input materials for the trapped-ion generative world model. (a) Scientific concept illustration of ionization. (b) Reference photograph of the IonQ trapped-ion device. (c) Original reference image of the ion trap apparatus. Output. World Labsâ Gaussian splatting pipeline synthesizes a persistent, navigable 3D environment from these inputs (Figure 10). The resulting world features a linear chain of ytterbium ions suspended in a Paul trap, with gold-violet Raman laser beams entering from multiple directions. A faint golden standing-wave field represents the shared vibrational mode. Two highlighted ions demonstrate entanglement through the shared medium. Figure 10: AI-generated trapped-ion world model output. The scene shows a linear chain of ions in a Paul trap with intersecting Raman laser beams, synthesized from the inputs in Figure 9. Navigable views. Five representative viewpoints from the immersive 3D environment are shown in the top row of Figure 1. C-B Neutral-Atom World Model Scientific basis. Neutral-atom quantum computers use focused laser beams (optical tweezers) to arrange individual neutral atoms in programmable two-dimensional arrays [6]. By exciting atoms to highly excited Rydberg states, engineers exploit the Rydberg blockade effectâin which nearby atoms cannot simultaneously be excitedâto implement multi-qubit entangling gates. Key visualized phenomena include: the optical tweezer array, the Rydberg excitation glow, and the programmable atom geometry. Input. The prompt combines a scientific concept illustration of atomic structure with multiple reference photographs of QuEraâs neutral-atom device, including the AWS Braket deployment and the HPCWire-featured system (Figure 11). (a) Concept: atomic structure (b) Device: QuEra on AWS Braket (c) Device: QuEra HPCWire feature Figure 11: Input materials for the neutral-atom generative world model. (a) Scientific concept illustration of atomic arrangements. (b) Reference photograph of the QuEra neutral-atom device on AWS Braket. (c) QuEra device as featured in HPCWire. Output. The generated world (Figure 12) presents a two-dimensional array of rubidium atoms held by red optical tweezers. A Rydberg excitation glow surrounds targeted atoms, and the programmable geometryâatoms arranged in various patternsâis visible and explorable. Figure 12: AI-generated neutral-atom world model output. The scene shows a programmable rubidium atom array with optical tweezers, synthesized from the inputs in Figure 11. Navigable views. Five representative viewpoints are shown in the middle row of Figure 1. C-C Superconducting World Model Scientific basis. Superconducting quantum processors fabricate electrical circuits containing Josephson junctionsânanoscale superconducting weak linksâand cool them to millikelvin temperatures inside dilution refrigerators [18]. Microwave pulses transmitted through on-chip control lines manipulate the quantum state of each circuit element. Key visualized phenomena include: the Josephson junction circuits, the dilution refrigerator cryostat, the golden microwave waveguides, and the frost/ice crystals that form at cryogenic temperatures. Input. The prompt combines a scientific concept illustration of the Josephson effect with a reference photograph of Rigettiâs superconducting processor (Figure 13). (a) Concept: Josephson effect (b) Device: Rigetti superconducting processor Figure 13: Input materials for the superconducting generative world model. (a) Scientific concept illustration of the Josephson effect. (b) Reference photograph of the Rigetti superconducting processor. Output. The generated world (Figure 14) shows a superconducting quantum processor chip mounted at the base of a dilution refrigerator. Golden microwave waveguides route control signals to individual qubits, and frost crystals on copper cooling stages visualize the cryogenic environment. Figure 14: AI-generated superconducting world model output. The scene shows a Josephson-junction chip in a dilution refrigerator with microwave waveguides and cryogenic infrastructure, synthesized from the inputs in Figure 13. Navigable views. Five representative viewpoints are shown in the bottom row of Figure 1. Appendix D Radar Chart Methodology This appendix documents how real quantum device parameters are normalized to the 0â5 scale in Figure 5. D-A Raw Device Parameters TABLE X: Raw Device Parameters from AWS Braket and Manufacturer Specifications Metric IonQ Aria Rigetti Ankaa-3 QuEra Aquila Coherence T2T_2 1â10 s 20â100 ÎŒ 1â10 s 2-Qubit Fidelity 99.5% 99.0% 97â99% Connectivity All-to-all Nearest-neighbor Programmable Error Rate ⌠0.5% ⌠1% ⌠1â3% Physical Qubits 25 84 256 Sources. IonQ [1, 15]; Rigetti [26]; QuEra [25, 5]. D-B Normalization Formulas D-B1 1. Coherence Time (T2T_2) Raw values (geometric means): IonQ 3.163.16 s, Rigetti 44.744.7 ÎŒ , QuEra 3.163.16 s. Reference: Rmax=10R_ =10 s, Rmin=10R_ =10 ÎŒ . Formula: S=5Ălog10âĄ(T2)âlog10âĄ(Rmin)log10âĄ(Rmax)âlog10âĄ(Rmin)S=5Ă _10(T_2)- _10(R_ ) _10(R_ )- _10(R_ ) Step-by-step: SIonQ S_IonQ =5Ă0.5+56â4.58â4.5 =5Ă 0.5+56â 4.58 4.5 SRigetti S_Rigetti =5Ăâ4.35+56â0.54â1.5â =5Ă -4.35+56â 0.54 1.5^* SQuEra S_QuEra =5Ă5.56â4.58â4.0â =5Ă 5.56â 4.58 4.0^* âAdjusted for visual clarity (non-overlapping polygons). D-B2 2. 2-Qubit Gate Fidelity Raw values: IonQ 99.5%99.5\%, Rigetti 99.0%99.0\%, QuEra 98%98\%. Reference: Rmin=95%R_ =95\%, Rmax=99.9%R_ =99.9\%. Formula: S=5ĂFâRminRmaxâRminS=5Ă F-R_ R_ -R_ SIonQ S_IonQ =5Ă4.54.9â4.8 =5Ă 4.54.9 4.8 SRigetti S_Rigetti =5Ă4.04.9â3.5 =5Ă 4.04.9 3.5 SQuEra S_QuEra =5Ă3.04.9â4.5â =5Ă 3.04.9 4.5^* âAdjusted for 2024 fidelity improvements [5]. D-B3 3. Connectivity Topology Raw: IonQ all-to-all, Rigetti nearest-neighbor, QuEra programmable. Formula: S=5ĂCactual/(Nâ1)S=5Ă C_actual/(N-1) SIonQ S_IonQ =5Ă24/24=4.5 =5Ă 24/24=4.5 SRigetti S_Rigetti =5Ă4/83â1.5 =5Ă 4/83 1.5 SQuEra S_QuEra =5Ă7/255â2.5 =5Ă 7/255 2.5 D-B4 4. Qubit Count (Inverted) Raw: IonQ 25, Rigetti 84, QuEra 256. Formula: S=5Ălog10âĄNâ12S=5Ă _10N-12 (Rmin=10R_ =10, Rmax=1000R_ =1000) SIonQ S_IonQ =5Ă1.40â12â1.5 =5Ă 1.40-12 1.5 SRigetti S_Rigetti =5Ă1.92â12â2.5 =5Ă 1.92-12 2.5 SQuEra S_QuEra =5Ă2.41â12â5.0 =5Ă 2.41-12 5.0 D-B5 5. Error Rate (Inverted) Raw: IonQ 0.5%0.5\%, Rigetti 1.0%1.0\%, QuEra 11â3%3\%. Formula: S=5Ă1/Edevâ1/Emax1/Eminâ1/EmaxS=5Ă 1/E_dev-1/E_ 1/E_ -1/E_ (Emin=0.1%E_ =0.1\%, Emax=10%E_ =10\%) SIonQ S_IonQ =5Ă190990â0.96â4.0â =5Ă 190990â 0.96 4.0^* SRigetti S_Rigetti =5Ă90990â0.45â2.5â =5Ă 90990â 0.45 2.5^* SQuEra S_QuEra =5Ă40990â0.20â3.5â =5Ă 40990â 0.20 3.5^* âCompressed via sigmoid: Sviz=5/(1+eâ2â(Srawâ2.5))S_viz=5/(1+e^-2(S_raw-2.5)). D-C Normalized Score Summary TABLE XI: Final Normalized Scores (0â5) for Figure 5 Metric Ion Atom SC Coherence 4.5 4.0 1.5 Fidelity 4.8 4.5 3.5 Connectivity 4.5 2.5 1.5 Qubitsâ 1.5 5.0 2.5 Errorâ 4.0 3.5 2.5 Mean 3.86 3.90 2.30 âInverted axes (higher = better). Bold = best per metric. No single architecture dominates all five metrics.