Inside TheQuantAI
Build Log

Introducing TheQuantCloud Beta — Free Quantum Computing in the Cloud

Today we're opening TheQuantCloud Beta to the public — a free cloud platform for quantum circuit execution. Sign up, generate an API key, and run quantum circuits on 5 simulator backends from your Python code or our web IDE.

What's included for free: 60 minutes of simulator time per month, $50 in beta credits, 10 QPU tasks/month, 5 quantum backends (Qiskit Aer, Cirq, PennyLane, local NumPy, and GPU simulator), unlimited saved circuits, and 5 API keys per account. No credit card required.

How it works: Submit a circuit (OpenQASM or QuantSDK Python) to our REST API at api.thequantcloud.com. The QuantRouter analyzes your circuit's features — qubit count, depth, gate types — and automatically selects the best simulator backend. Results come back with measurement counts, probabilities, and execution metadata.

Architecture: A FastAPI modular monolith with 19 API endpoints, deployed on Railway ($5/month). PostgreSQL on Supabase (free tier) for all persistence — users, circuits, jobs, results, usage tracking, and routing logs. Supabase Auth handles authentication (email + GitHub OAuth). The entire platform costs under $7/month to run — 99.5% cheaper than the original AWS specification.

Get started in 60 seconds:

1. Sign up at studio.thequantcloud.com/signup
2. Generate an API key from the Connect page
3. pip install thequantsdk
4. Set QUANTCLOUD_API_KEY and run your circuit

What's next: Real QPU backends (IBM Quantum, IonQ) via cloud credits, a Stripe-powered billing system, and the ML-trained QuantRouter v2 that learns from actual execution outcomes. Follow us at @TheQuantAI for updates.

QuantStudio v0.1 — A Web IDE for Quantum Computing

Today we're launching QuantStudio, a full-featured browser-based quantum development environment. Built with Next.js, Monaco Editor, and our own QuantSDK backend, it brings quantum circuit development to your browser — no local setup required.

What's included: Monaco code editor with QuantSDK autocomplete, SVG circuit visualization, interactive result histograms with counts/probability toggle, 16 ready-to-run circuit templates across 7 categories (entanglement, algorithms, transforms, protocols, variational, utility, QML), backend comparison dashboard with 8 quantum backends, user authentication, and save/load circuit management.

Under the hood: The frontend is React 19 + Next.js 16 with Zustand state management and TanStack Query. The execution backend is FastAPI running QuantSDK in a sandboxed Python environment. Every circuit you write in the editor is sent to the backend, executed on our simulator, and results flow back to the rich visualization panel in real-time.

Introducing QuantSDK v0.1

QuantSDK v0.1 is our open-source quantum development kit — a clean, Pythonic SDK that lets you write quantum circuits once and run them on any backend. It ships with 50+ quantum gates, a high-performance statevector simulator, and seamless interop with Qiskit, Cirq, PennyLane, and OpenQASM.

By the numbers: 658 tests passing, 22 Jupyter example notebooks covering everything from Bell states to variational algorithms, full MkDocs documentation with API reference, tutorials, and getting-started guides. The SDK supports framework import/export, GPU simulation via cuQuantum, and backend adapters for IBM Quantum and IonQ.

Install it now: pip install thequantsdk

TheQuantCloud architecture and rollout plan

TheQuantCloud is our "AWS for Quantum" — a hardware-agnostic, AI-first cloud platform for quantum computing. The architecture follows a layered design: QuantSDK at the foundation (open-source), QuantStudio as the developer experience layer, and the cloud platform providing orchestration, intelligent routing, and enterprise features on top.

Phase 1 progress: Sprint 1 (Core Foundation) and Sprint 2 (SDK & Studio Launch) are complete. We've built 8 backend adapters covering simulators (CPU, GPU, noisy) and real QPU providers (IBM, IonQ, Rigetti, Quantinuum). Sprint 3 targets the full cloud beta with Kubernetes orchestration, job queuing, QuantRouter ML model, and Stripe/Razorpay billing.

From notebooks to production workflows

Moving quantum workloads from Jupyter notebook experiments to reliable production systems requires tooling that most platforms lack. Our approach: sandboxed execution with resource limits, structured result objects with metadata (circuit depth, gate count, entropy), export capabilities (JSON, CSV, SVG), and a backend comparison system for informed hardware selection.