Product ownership, domain direction, and final review remain human-led.
About QC+AI Studio
A public QC+AI learning platform built for technical trust, not just surface polish.
QC+AI Studio is a graduate-level learning environment for quantum computing and artificial intelligence. It turns curated proceedings, applied industry material, and lecture assets into a structured web product with modules, lessons, grounded Q&A, projects, and learner analytics.
QC+AI Studio is currently a focused seven-lesson studio track. It is designed as an intensive starter-to-project path for advanced learners, not yet as a full fifteen-week semester.
Structured curriculum blocks in the current public studio track.
Focused learning units connected to search, flashcards, quizzes, and projects.
3 documents and 3 videos shape the public curriculum.
Mission
Why this product exists
Most QC+AI resources split in the wrong direction: either they stay too abstract to help engineers reason about real systems, or they oversimplify quantum claims into marketing language. This platform is meant to create a middle path anchored in evidence, architecture, and practical skepticism.
Public promise
What the site should make clear
- The curriculum is source-grounded and inspectable.
- The course prioritizes hybrid-system design under hardware limits.
- The product is honest about what is fully operational, what is guest-mode, and what is still environment-gated.
Creator context
Ownership and contact
Nay Linn Aung remains responsible for product direction, publication decisions, and final review. Questions about attribution, evaluation, or the public course experience can be sent to na27@hood.edu.
Editorial method
How the content is framed
The site does not treat every source equally. Proceedings-style research, applied industry synthesis, and lecture media play different roles inside the learning path, and the product now says so explicitly.
Reading lens
How to interpret the lessons
- Treat the quantum component as a bounded systems decision, not as a blanket replacement for the classical stack.
- Ask what physical bottleneck moved: routing depth, graph size, calibration burden, data encoding, or validation complexity.
- Read every application claim against a classical baseline and a concrete deployment constraint.
Industry synthesis note
Why Module 5 is labeled carefully
Module 5 is intentionally framed as applied and commercial synthesis. It draws from the curated industry-use-case source to teach adoption patterns, sector readiness, and deployment constraints, rather than presenting itself as proceedings-style peer-reviewed evidence.
Verification and review
How the product is kept honest
A public learning product should not ask visitors to trust vague claims. The implementation is backed by tests, deployable infrastructure, source-linked retrieval, and explicit disclosure of environment-gated capabilities.
Practice
Human review stays responsible for scope, architecture, acceptance criteria, and final publication.
Practice
AI assistance accelerated implementation, refinement, testing, infrastructure, and documentation work across the stack.
Practice
Claims on the public site are strongest when they can be tied back to the live codebase, a source asset, or a reproducible test run.
Architecture snapshot
Full-stack engineering footprint
The public site is the frontend of a larger product surface that includes content assembly, retrieval, analytics, project workflows, and secure media delivery.
Backend
FastAPI service architecture
The platform exposes a substantial API surface for lessons, grounded QA, analytics, projects, and interactive practice modes.
- 11 route groups cover content, auth, search, QA, analytics, assets, arena, builder, insights, projects, and admin flows.
- SQLAlchemy and Alembic support persistence across learner activity, notes, reviews, and progress data.
- Authenticated asset delivery includes byte-range streaming so MP4 playback and seeking work reliably.
AI and retrieval
Grounded intelligence instead of generic chat
QC+AI Studio uses retrieval-first AI services so niche quantum topics stay tied to evidence instead of model improvisation.
- LangChain and OpenAI GPT-4.1-mini drive citation-first Q&A against the curated corpus when the OpenAI key is provisioned.
- Pinecone-backed hybrid retrieval activates only when OpenAI and Pinecone secrets are present; otherwise the site stays on grounded lexical fallback.
- Adaptive insights, skill-gap reporting, and next-step recommendations extend the platform beyond static content delivery.
Frontend and cloud
Modern app delivery with production posture
The learner experience is built as a real web application with secure public and protected surfaces, not as a static microsite.
- Next.js 16.2.1, React 19.2.4, TypeScript, and App Router power the frontend experience.
- Proxy-based guest auth and CSRF protection keep public study surfaces usable without weakening mutation safety.
- Docker, Cloud Run, Cloud SQL, GCS, Secret Manager, and Cloud DNS form the intended production deployment stack.
Explore next
Read the curriculum, then inspect the implementation details
The curriculum hub now covers prerequisites, project rubrics, public guest access, and methodological notes. The attribution page covers the human-directed AI-assisted build process in more detail.