# cuQuantum Python

## Documentation

Please visit the [NVIDIA cuQuantum Python documentation](https://docs.nvidia.com/cuda/cuquantum/python).


## Building

### Requirements

Build-time dependencies of the cuQuantum Python package and some versions that
are known to work are as follows:

* CUDA Toolkit 11.x
* cuQuantum 22.07+
* cuTENSOR 1.5.0+
* Python 3.8+
* Cython - e.g. 0.29.21
* [packaging](https://packaging.pypa.io/en/latest/)

### Install cuQuantum Python from conda-forge

If you already have a Conda environment set up, it is the easiest to install cuQuantum Python from the conda-forge channel:
```
conda install -c conda-forge cuquantum-python
```
The Conda solver will install all required dependencies for you.

### Install cuQuantum Python from PyPI

Alternatively, assuming you already have a Python environment set up (it doesn't matter if it's a Conda env or not),
you can also install cuQuantum Python this way:

```
pip install cuquantum-python
```
The `pip` solver will also install both cuTENSOR and cuQuantum for you.

Note: To properly install the wheels the environment variable `CUQUANTUM_ROOT` must not be set.

### Install cuQuantum Python from source

To compile and install cuQuantum Python from source, please follow the steps below:

1. Set `CUDA_PATH` to point to your CUDA installation
2. Set `CUQUANTUM_ROOT` to point to your cuQuantum installation
3. Set `CUTENSOR_ROOT` to point to your cuTENSOR installation
4. Make sure CUDA, cuQuantum and cuTENSOR are visible in your `LD_LIBRARY_PATH`
5. Run `pip install -v .`

Notes:
- For the `pip install` step, adding the `-e` flag after `-v` would allow installing the package in-place (i.e., in "editable mode" for testing/developing).
- If `CUSTATEVEC_ROOT` and `CUTENSORNET_ROOT` are set (for the cuStateVec and the cuTensorNet libraries, respectively), they overwrite `CUQUANTUM_ROOT`.
- For local development, set `CUQUANTUM_IGNORE_SOLVER=1` to ignore the dependency on the `cuquantum` wheel.


## Running

### Requirements

Runtime dependencies of the cuQuantum Python package include:

* An NVIDIA GPU with compute capability 7.0+
* Driver: Linux (450.80.02+)
* CUDA Toolkit 11.x
* cuQuantum 22.07+
* cuTENSOR 1.5.0+
* Python 3.8+
* NumPy v1.19+
* CuPy v9.5.0+ (see [installation guide](https://docs.cupy.dev/en/stable/install.html))
* PyTorch v1.10+ (optional, see [installation guide](https://pytorch.org/get-started/locally/))
* Qiskit v0.24.0+ (optional, see [installation guide](https://qiskit.org/documentation/getting_started.html))
* Cirq v0.6.0+ (optional, see [installation guide](https://quantumai.google/cirq/install))

If you install everything from conda-forge, the dependencies are taken care for you (except for the driver).

If you install the pip wheels, cuTENSOR and cuQuantum (but not CUDA Toolkit or the driver,
please make sure the CUDA libraries are discoverable through your `LD_LIBRARY_PATH`) are installed for you.

If you build cuQuantum Python from source, please make sure the paths to the cuQuantum and cuTENSOR libraries are added
to your `LD_LIBRARY_PATH` environment variable.

Known issues:
- If a system has multiple copies of cuTENSOR, one of which is installed in a default system path, the Python runtime could pick it up despite cuQuantum Python is linked to another copy installed elsewhere, potentially causing a version-mismatch error. The proper fix is to remove cuTENSOR from the system paths to ensure the visibility of the proper copy. **DO NOT ATTEMPT** to use `LD_PRELOAD` to overwrite it --- it could cause hard to debug behaviors!
- In certain environments, if PyTorch is installed `import cuquantum` could fail (with a segmentation fault). It is currently under investigation and a temporary workaround is to import `torch` before importing `cuquantum`.

### Samples

Samples for demonstrating the usage of both low-level and high-level Python APIs are
available in the `samples` directory. The low-level API samples are 1:1 translations of the corresponding
samples written in C. The high-level API samples demonstrate pythonic usages of the cuTensorNet
library in Python.


## Testing

If pytest is installed, typing `pytest tests` at the command prompt in the Python source root directory will
run all tests. Some tests would be skipped if `cffi` is not installed or if the environment
variable `CUDA_PATH` is not set.


## Citing cuQuantum

Pleae click this Zenodo badge to see the citation format: [![DOI](https://zenodo.org/badge/435003852.svg)](https://zenodo.org/badge/latestdoi/435003852)