docs: Add CONTRIBUTING.md

.github/workflows/functional_tests.yaml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3

.gitignore

@@ -336,3 +336,15 @@ es_runs/
 .vscode-upload.json
 
 mnist_data/
+
+configs.yml
+debug_qiskit.txt
+fix_note.txt
+h2_new.txt
+max-acc-valid.pt
+model.pt
+.cursor/rules/*.mdc
+
+.cursorignore
+.cursor/
+.github/copilot-instructions.md

CONTRIBUTING.md

@@ -0,0 +1,116 @@
+# Contributing to TorchQuantum
+
+Thank you for your interest in contributing to TorchQuantum! This document provides guidelines and instructions for contributing.
+
+## Getting Started
+
+### Setting Up Development Environment
+
+1. Fork the repository on GitHub
+2. Clone your fork locally:
+   ```bash
+   git clone https://github.com/YOUR_USERNAME/torchquantum.git
+   cd torchquantum
+   ```
+3. Install in development mode:
+   ```bash
+   pip install --editable .
+   ```
+
+### Branch Strategy
+
+- **main**: Stable release branch
+- **dev**: Development branch for integrating features
+
+**Always create pull requests to the `dev` branch, not `main`.**
+
+## Making Contributions
+
+### Pull Request Process
+
+1. Create a new branch from `dev` for your feature or fix:
+   ```bash
+   git checkout dev
+   git pull origin dev
+   git checkout -b feature/your-feature-name
+   ```
+
+2. Make your changes following the code style guidelines below
+
+3. Test your changes thoroughly:
+   ```bash
+   # Run existing tests
+   pytest tests/
+
+   # For new features, add corresponding tests
+   ```
+
+4. Commit your changes with clear, descriptive messages:
+   ```bash
+   git commit -m "Brief description of changes"
+   ```
+
+5. Push to your fork and create a pull request:
+   ```bash
+   git push origin feature/your-feature-name
+   ```
+
+6. In your PR description:
+   - Describe what changes you made and why
+   - Reference any related issues (e.g., "Fixes #123")
+   - Include test results or examples if applicable
+
+7. Ping a maintainer for review by tagging them in the PR
+
+### Code Style Guidelines
+
+- Follow [PEP 8](https://pep8.org/) style guidelines
+- Use meaningful variable and function names
+- Add docstrings to new functions and classes
+- Keep functions focused and modular
+- Use type hints where appropriate
+
+### Testing Requirements
+
+- **All changes must pass existing tests**
+- **New features must include corresponding tests**
+- Test edge cases and error conditions
+- Ensure backward compatibility when modifying existing functionality
+
+### Documentation
+
+- Update docstrings for any modified functions
+- Add examples for new features
+- Update README if adding major functionality
+
+## Types of Contributions
+
+### Bug Fixes
+
+1. Check if the bug is already reported in [Issues](https://github.com/mit-han-lab/torchquantum/issues)
+2. If not, create a new issue describing the bug
+3. Reference the issue in your PR
+
+### New Features
+
+1. Open an issue first to discuss the feature
+2. Wait for maintainer feedback before starting implementation
+3. Follow the PR process above
+
+### Documentation
+
+- Improvements to README, docstrings, or examples are always welcome
+- For major documentation changes, open an issue first
+
+## Code of Conduct
+
+- Be respectful and inclusive
+- Provide constructive feedback
+- Help others learn and grow
+
+## Questions?
+
+- Open an issue for questions about contributing
+- Tag maintainers for guidance on complex changes
+
+Thank you for helping improve TorchQuantum!

README.md

@@ -55,12 +55,12 @@ Simulate quantum computations on classical hardware using PyTorch. It supports s
 Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum neural networks.
 #### Differences from Qiskit/Pennylane
 
-Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.
+Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tensorized processing.
 
 ## News
 - Torchquantum is used in the winning team for ACM Quantum Computing for Drug Discovery Challenge.
-- Torchquantum is highlighted in [UnitaryHack](https://unitaryhack.dev/projects/torchquantum/).
-- TorchQuantum received [UnitaryFund](https://unitary.fund/).
+- Torchquantum is highlighted in [unitaryHACK](https://2024.unitaryhack.dev/projects/torchquantum/).
+- TorchQuantum received a [Unitary Foundation microgrant](https://unitary.foundation/grants/).
 - TorchQuantum is integrated to [IBM Qiskit Ecosystem](https://qiskit.github.io/ecosystem/).
 - TorchQuantum is integrated to [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
 - v0.1.8 Available!

examples/QuantumNAS/quantumnas.ipynb

@@ -4056,7 +4056,8 @@
       "toc_visible": true
     },
     "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": "torchquantum",
+      "language": "python",
       "name": "python3"
     },
     "language_info": {
@@ -4069,7 +4070,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.16"
+      "version": "3.9.20"
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {

examples/gradient_pruning/callbacks.py

@@ -37,7 +37,7 @@
 from torchpack.utils.logging import logger
 from torchpack.utils.typing import Trainer
 from torchpack import distributed as dist
-from torchquantum.super_utils import get_named_sample_arch
+from torchquantum.algorithm.quantumnas.super_utils import get_named_sample_arch
 from torchquantum.util import legalize_unitary
 
 

examples/regression/new_run_regression.py

@@ -46,13 +46,13 @@
 
 
 def gen_data(L, N):
-    omega_0 = np.zeros([2**L], dtype="complex_")
+    omega_0 = np.zeros([2**L], dtype=complex)
     omega_0[0] = 1 + 0j
 
-    omega_1 = np.zeros([2**L], dtype="complex_")
+    omega_1 = np.zeros([2**L], dtype=complex)
     omega_1[-1] = 1 + 0j
 
-    states = np.zeros([N, 2**L], dtype="complex_")
+    states = np.zeros([N, 2**L], dtype=complex)
 
     thetas = 2 * np.pi * np.random.rand(N)
     phis = 2 * np.pi * np.random.rand(N)

examples/regression/run_regression.py

@@ -40,13 +40,13 @@
 
 
 def gen_data(L, N):
-    omega_0 = np.zeros([2**L], dtype="complex_")
+    omega_0 = np.zeros([2**L], dtype=complex)
     omega_0[0] = 1 + 0j
 
-    omega_1 = np.zeros([2**L], dtype="complex_")
+    omega_1 = np.zeros([2**L], dtype=complex)
     omega_1[-1] = 1 + 0j
 
-    states = np.zeros([N, 2**L], dtype="complex_")
+    states = np.zeros([N, 2**L], dtype=complex)
 
     thetas = 2 * np.pi * np.random.rand(N)
     phis = 2 * np.pi * np.random.rand(N)

examples/save_load_example/save_load.py

@@ -119,7 +119,7 @@ def save_load2():
 
     torch.save(model, "model_whole.pt")
 
-    model2 = torch.load("model_whole.pt")
+    model2 = torch.load("model_whole.pt", weights_only=False)
     y2 = model2(x)
     print(y2)
     assert torch.equal(y, y2)

requirement_update.txt

@@ -0,0 +1,24 @@
+
+dill
+matplotlib
+nbsphinx
+numpy
+
+opt_einsum
+pathos
+pylatexenc
+pyscf
+qiskit
+recommonmark
+qiskit_ibm_runtime
+qiskit-aer
+
+scipy
+setuptools
+tensorflow
+torch
+torchdiffeq
+torchpack
+torchquantum
+torchvision
+tqdm

requirements.txt

@@ -8,10 +8,10 @@ opt_einsum
 pathos>=0.2.7
 pylatexenc>=2.10
 pyscf>=2.0.1
-qiskit>=0.39.0,<1.0.0
+qiskit==1.4.3
 recommonmark
-qiskit_ibm_runtime==0.20.0
-qiskit-aer==0.13.3
+qiskit_ibm_runtime
+qiskit-aer
 
 scipy>=1.5.2
 setuptools>=52.0.0
@@ -21,4 +21,4 @@ torchdiffeq>=0.2.3
 torchpack>=0.3.0
 torchquantum>=0.1
 torchvision>=0.9.0.dev20210130
-tqdm>=4.56.0
+tqdm>=4.56.0