Agentic Development

TensorCircuit-NG is the world’s first AI-native quantum programming platform, purpose-built for agentic research and automated scientific discovery.

🚀 Experience Agent-Native Discovery

Click here to see how AI agents autonomously solve complex quantum problems in TensorCircuit-NG.

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Platform Overview

Review the broader TensorCircuit-NG architecture, performance evidence, and adoption landscape.

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Why Work Within the Repository?

To write scripts and applications efficiently with AI coding agents (e.g., ClaudeCode, Cursor, Codex, Antigravity, OpenCode), we strongly recommend working directly within the local repository.

  1. Rich Context: The repository contains over 100 scripts in examples/ and extensive test cases in tests/. These provide essential references that significantly reduce AI hallucinations and help the agent understand idiomatic usage.

  2. Built-in Rules: We provide a dedicated AGENTS.md file. It serves as the “handbook” (similar to CLAUDE.md) for AI agents, defining coding standards and best practices.

  3. Specialized Agentic Skills: The .agents/skills/ directory contains specialized workflows to guide AI assistants on complex, multi-step tasks.

Specialized Agentic Skills

TensorCircuit-NG includes built-in agentic skills that can be activated by compatible AI agents to perform advanced tasks:

  • arxiv-reproduce: Autonomously reproduces arXiv papers with standardized output and code quality validation.

  • performance-optimize: Scientific execution and memory optimization workflow (JAX scanning, vectorized parallelism, etc.).

  • tc-rosetta: End-to-end framework translation (from Qiskit, PennyLane, etc.) with intrinsic mathematical intent rewriting.

  • tutorial-crafter: Transforms raw scripts into comprehensive, narrative-driven educational tutorials.

  • demo-generator: Transforms scripts into interactive, high-performance Streamlit GUI applications.

  • code-reviewer: Autonomously reviews and refactors code for mathematical correctness and performance.

  • sanity-checker: Systematic audit and refactoring to reduce technical debt, improve abstractions, and ensure codebase health.

  • meta-explorer: High-intensity autonomous research agent for circuit architecture and optimization strategy discovery (VQE, QML, QAOA, etc.).

Initial Prompt for Empty Workspace Setup

When starting in a completely empty folder/workspace with an AI agent, you can copy and paste the following initial prompt to bootstrap the agent. This instructs the agent to clone the repository, read coding guidelines/skills, and execute tasks accordingly:

You are an AI coding assistant. Please follow these steps to set up our project workspace and solve my requests:

1. Run `git clone https://github.com/tensorcircuit/tensorcircuit-ng.git .` to download the repository in this empty folder.
2. Install the git-cloned TensorCircuit-NG library locally in the appropriate Python environment. If unsure which Python environment is appropriate, ask the user.
3. Read `AGENTS.md` at the root of the repository to understand the coding standards.
4. Review the available agentic workflow scripts and skill files under `.agents/skills/` to see if a specialized workflow is suitable for the current task.
5. Reference existing implementations in `examples/` and test cases in `tests/` to write differentiable, and JIT-friendly code.
6. Respond to my later requests, write code, and execute experiments/tests to analyse the results.

Let me know when the initialization is complete and ask for my requests.

AI-Native Documentation

One can also refer to AI-native docs for tensorcircuit-ng: Devin Deepwiki, Google Code Wiki, and Context7 MCP.