rename to AGENTS.md

Author: refraction-ray

llm.md AGENTS.mdllm.md renamed to AGENTS.md

@@ -5,21 +5,23 @@
 TensorCircuit is a **Tensor Network-first**, **Multi-Backend** quantum computing framework. When contributing to this codebase, you must adhere to the following architectural principles:
 
 ### Unified Backend Interface
+
 - **Rule**: Never use backend-specific libraries (numpy, tensorflow, jax) directly in core logic.
 - **Pattern**: Use the `tc.backend` abstraction for all tensor operations.
 - **Example**: Use `tc.backend.sin(x)` instead of `np.sin(x)` or `tf.math.sin(x)`. This ensures code runs seamlessly on TensorFlow, JAX, PyTorch, and NumPy.
 
 ### Differentiable Programming (AD)
+
 - **Rule**: All core components must be differentiable.
 - **Pattern**: Avoid operations that break the computation graph (e.g., converting to numpy inside a differentiable function, using in-place assignments on tensors).
 - **Goal**: End-to-end differentiability allows variational algorithms and gradient-based optimization to work out of the box.
 
 ### JIT-First Optimization
+
 - **Rule**: Write code that is JIT-compilable (Just-In-Time).
 - **Pattern**: Avoid Python control flow (if/else) that depends on tensor values. Use `tc.backend.cond` or `tc.backend.switch` if necessary, or structure code to be statically analyzable.
 - **Benefit**: This enables massive speedups on JAX and TensorFlow backends.
 
-
 ## Repository Structure
 
 - `tensorcircuit/`: Core package source code.
@@ -60,7 +62,6 @@ TensorCircuit is a **Tensor Network-first**, **Multi-Backend** quantum computing
 ### Configuration Files
 
 1. `requirements/` - Contains various requirement files:
-
    - [requirements.txt](requirements/requirements.txt) - Core dependencies
    - [requirements-dev.txt](requirements/requirements-dev.txt) - Development tools
    - [requirements-extra.txt](requirements/requirements-extra.txt) - Optional dependencies
@@ -70,23 +71,34 @@ TensorCircuit is a **Tensor Network-first**, **Multi-Backend** quantum computing
 
 3. `.pylintrc` - Code style enforcement with specific rules enabled
 
-
 ## AI Agent Best Practices
 
 ### Code Navigation
+
 - **Search First**: The codebase is extensive. Search for class definitions (e.g., `class Hamiltonian`) rather than guessing file paths.
 - **Check Tests**: `tests/test_*.py` files are the ultimate source of truth for how APIs are intended to be used.
 
 ### Coding Standards
+
 - **Linting**: We enforce strict **Pylint** and **Black** formatting.
   - Run `bash check_all.sh` before submitting changes.
   - Target Pylint score: 10.0/10.
 - **Type Hinting**: Use type hints liberally to aid static analysis.
 - **Documentation**: Write clear docstrings (reStructuredText format) for all public APIs.
 
+### Coding Style Suggestions
+
+Follow these rules for all code changes in this repository:
+
+- Minimize comments; be concise; code should be self-explanatory and self-documenting.
+- Comments should be useful, for example, comments that remind the reader about some global context that is non-obvious and can't be inferred locally.
+- Match existing code style and architectural patterns of the codebase.
+- If uncertain, choose the simpler, more concise implementation.
+
 ### Common Workflows
 
 #### 1. Running Tests
+
 ```bash
 # Run all tests (auto-parallelized)
 pytest -n auto
@@ -96,6 +108,7 @@ pytest tests/test_hamiltonians.py
 ```
 
 #### 2. Checking Code Quality
+
 ```bash
 # Check formatting
 black . --check
@@ -145,5 +158,3 @@ pylint tensorcircuit/quantum.py
 
 - Devin Deepwiki: https://deepwiki.com/tensorcircuit/tensorcircuit-ng
 - Context7 MCP: https://context7.com/tensorcircuit/tensorcircuit-ng
-
-

README.md

@@ -44,15 +44,17 @@ For beginners, please refer to [quantum computing lectures with TC-NG](https://g
 To write TC-NG scripts and applications efficiently with AI coding agents (e.g., Claude Code, Cursor, Antigravity), we strongly recommend **working directly within the local tensorcircuit-ng repository** rather than an empty folder.
 
 1.  **Rich Context:** The 100+ scripts in `examples/` and extensive test cases in `tests/` provide essential references that significantly reduce AI hallucinations.
-2.  **Built-in Rules:** We provide a dedicated **[`llm.md`](/llm.md)** file. It serves as the "handbook" (i.e. `CLAUDE.md`) for AI agents, defining coding standards and best practices to ensure the generated code is idiomatic.
+2.  **Built-in Rules:** We provide a dedicated **[`AGENTS.md`](/AGENTS.md)** file. It serves as the "handbook" (i.e. `CLAUDE.md`) for AI agents, defining coding standards and best practices to ensure the generated code is idiomatic.
 
 **Recommended Workflow:**
 
 1.  Clone the repository: `git clone https://github.com/tensorcircuit/tensorcircuit-ng.git`
 2.  Switch to a local playground branch: `git checkout -b my-playground` in case messing up with the original repository.
 3.  Open the repository folder in your AI IDE. And you are ready to start writing TC-NG scripts.
 
-Now, enjoy implementing quantum algorithms entirely through natural language.
+Now, enjoy implementing quantum algorithms entirely through natural language!
+
+### Quick Demos
 
 The following are some minimal demos.
 
@@ -198,9 +200,7 @@ We also have [Docker support](/docker).
   - Support **Fermion Gaussian state** simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.
   - Support **qudits simulation** for tensor network and MPS approximation modes.
   - Support **parallel** quantum circuit evaluation across **multiple GPUs**.
-
   - Highly customizable **noise model** with gate error and scalable readout error.
-
   - Support for **non-unitary** gate and post-selection simulation.
 
   - Support **real quantum devices access** from different providers.