Quantum computing software is notoriously hard to write.
If you want to simulate a deep quantum neural network or research a new algorithm, you don't just need to understand Hamiltonian dynamics and Hilbert spaces. You also need to be a High-Performance Computing (HPC) expert—wrestling with GPU memory limits (OOMs), vectorization, JIT compilation staging times, and tensor network contraction paths.
For years, we've provided developers with the tools to do this via TensorCircuit-NG, our next-generation open-source, high-performance quantum software framework.
But tools are passive. You still have to do the heavy lifting.
Today, we are changing the paradigm. We are thrilled to announce that TensorCircuit-NG is now the world’s first AI-native quantum programming platform purpose-built for agentic quantum research and automated scientific discovery. By natively integrating skills directly into our repository, your quantum framework now comes with a built-in HPC engineer, a theoretical physicist, and a technical writer.
The Paradigm Shift: Agent-Ready Architecture 🧠
Most AI coding assistants do "line-by-line" translations or generate boilerplate. That doesn't work in quantum simulation, where a poorly placed for loop can increase compilation time from 2 seconds to 2 hours.
Instead of writing endless tutorials on "best practices," we embedded our framework knowledge directly into the repository as Agentic Skills.
If you clone the latest TensorCircuit-NG repo, you'll notice a new directory structure:
Plaintext
.agents/skills/
├── arxiv-reproduce/
├── performance-optimize/
├── tc-rosetta/
└── tutorial-crafter/
These aren't just prompts; they are strict, engineering-bound AI workflows. Let's break down the four superpowers you now have access to right out of the box.
1. /arxiv-reproduce: From arXiv ID to JAX-Accelerated Code in Minutes 📄➡️💻
The gap between reading a cutting-edge quantum machine learning paper on arXiv and actually writing the code to reproduce it is huge.
With the arxiv-reproduce skill, you simply hand the AI an arXiv link. The agent will:
- Extract the physical intent (the Ansatz, the Hamiltonian, the loss function).
- Intelligently scale down the qubit count so it runs on your local machine without blowing up your RAM.
- Generate idiomatically correct, JAX-accelerated TensorCircuit-NG code.
- Automatically run formatting (
black), linting (pylint), and execute the script to save the reproduced figure into a standardizedoutputs/folder.
2. /performance-optimize: Your Built-in HPC Architect ⚡
Got a quantum script that takes forever to compile or crashes with an Out-of-Memory (OOM) error?
The performance-optimize agent scans your code to identify bottlenecks. It knows the dark arts of quantum HPC: it will automatically eradicate Python loops in favor of jax.vmap, wrap your deep quantum layers in jax.lax.scan to slash JIT staging time, inject jax.checkpoint to trade compute for memory during backpropagation, and seamlessly switch to cotengra for optimal tensor network contraction paths. It even runs A/B benchmarks to prove the speedup!
3. /tc-rosetta: End-to-End Cross-Ecosystem Translation 🌍
Migrating from older, object-oriented quantum frameworks (like Qiskit or PennyLane) to a modern, differentiable, functional framework like TensorCircuit-NG is a steep mental shift.
tc-rosetta does not do naive line-by-line syntax swapping. It performs end-to-end intent extraction. It reads your slow, loop-heavy legacy script, understands the math behind it, and rewrites it from scratch using pure JAX-native paradigms. It then executes both scripts and hands you a benchmark report (e.g., "Execution time reduced from 300 seconds to 0.2 seconds").
4. /tutorial-crafter: Automated High-Quality Documentation 📝
Writing docs is the bane of every open-source contributor. What if the code could explain itself?
Point tutorial-crafter at any raw TensorCircuit-NG script. It will analyze the physical background and the code, then generate a beautiful, narrative-driven tutorial in both Markdown and HTML formats. It chunks the code logically, adds LaTeX formulas for the physics theory, and explicitly points out the HPC programming highlights (e.g., "Notice how we used vmap here instead of a loop..."). It generates documentation that rivals hand-crafted, premium tutorials.
How to Experience the Magic ✨
Because these skills are built on the open standard, getting started is zero-friction.
- Clone the TensorCircuit-NG repository.
- Open your terminal in the repo root.
- Fire up your AI agent and simply call a skill:
/performance-optimize examples/my_slow_circuit.py
You are no longer just writing code; you are directing an autonomous digital research team.
Welcome to the era of Agentic Quantum Software Engineering. We can't wait to see what you discover. Check out the repo, give us a star, and let the AI handle the boilerplate while you focus on the physics! 🌌
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