Update installation instructions

README.md

@@ -3,43 +3,41 @@
 [![Slack](https://img.shields.io/badge/Slack-4A154B?style=for-the-badge&logo=slack&logoColor=white)](https://tketusers.slack.com/join/shared_invite/zt-18qmsamj9-UqQFVdkRzxnXCcKtcarLRA#)
 [![Stack Exchange](https://img.shields.io/badge/StackExchange-%23ffffff.svg?style=for-the-badge&logo=StackExchange)](https://quantumcomputing.stackexchange.com/tags/pytket)
 
-[Pytket](https://tket.quantinuum.com/api-docs/index.html) is a python module for interfacing
+[Pytket](https://docs.quantinuum.com/tket/api-docs/) is a python module for interfacing
 with tket, a quantum computing toolkit and optimising compiler developed by Quantinuum.
 
 [cuTensorNet](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html) is a
 high-performance library for tensor network computations, developed by NVIDIA.
 It is part of the [cuQuantum](https://docs.nvidia.com/cuda/cuquantum/latest/index.html) SDK -
-a high-performance library aimed at quantum circuit simulations on the NVIDIA GPU chips,
-consisting of two major components:
- - `cuStateVec`: a high-performance library for state vector computations.
- - `cuTensorNet`: a high-performance library for tensor network computations.
-
-Both components have both C and Python API.
+a high-performance library aimed at quantum circuit simulations on the NVIDIA GPUs.
 
 `pytket-cutensornet` is an extension to `pytket` that allows `pytket` circuits and
 expectation values to be simulated using `cuTensorNet` via an interface to
 [cuQuantum Python](https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html).
 
-Currently, only single-GPU calculations are supported, but a multi-GPU execution will be
-implemented in the due course using `mpi4py` library.
-
 Some useful links:
-- [API Documentation](https://tket.quantinuum.com/extensions/pytket-cutensornet/)
+- [API Documentation](https://docs.quantinuum.com/tket/extensions/pytket-cutensornet/)
 
 ## Getting started
 
 `pytket-cutensornet` is available for Python 3.10, 3.11 and 3.12 on Linux.
-In order to use it, you need access to a Linux machine with an NVIDIA GPU of
-Compute Capability +7.0 (check it [here](https://developer.nvidia.com/cuda-gpus)) and first
-install `cuQuantum Python` following their installation
-[instructions](https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html).
-This will include the necessary dependencies such as CUDA toolkit. Then, to install
-`pytket-cutensornet`, run:
+In order to use it, you need access to a Linux machine (or WSL) with an NVIDIA GPU of
+Compute Capability +7.0 (check it [here](https://developer.nvidia.com/cuda-gpus)).
+You will need to install `cuda-toolkit` and `cuquantum-python` before `pytket-cutensornet`;
+for instance, in Ubuntu 24.04:
 
 ```shell
+sudo apt install cuda-toolkit
+pip install cuquantum-python
 pip install pytket-cutensornet
 ```
 
+Alternatively, you may install cuQuantum Python following their
+[instructions](https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html) using `conda-forge`.
+This will include the necessary dependencies from CUDA toolkit. Then, you may install
+`pytket-cutensornet` using `pip`.
+
+
 ## Bugs, support and feature requests
 
 Please file bugs and feature requests on the Github
@@ -104,10 +102,10 @@ with a set of style requirements (listed in `.pylintrc`). You should run
 
 To run the tests for a module:
 
-1. `cd` into that module's `tests` directory;
-2. ensure you have installed `pytest` and any other modules listed in
-the `test-requirements.txt` file (all via `pip`);
-3. run `pytest`.
+```shell
+pip install -r tests/test-requirements.txt
+pytest tests/
+```
 
 When adding a new feature, please add a test for it. When fixing a bug, please
 add a test that demonstrates the fix.

_metadata.py

@@ -1,2 +1,2 @@
-__extension_version__ = "0.10.0"
+__extension_version__ = "0.10.1"
 __extension_name__ = "pytket-cutensornet"

docs/changelog.rst

@@ -3,11 +3,11 @@
 Changelog
 ~~~~~~~~~
 
-Unreleased
-----------
+0.10.1 (December 2024)
+----------------------
 
-* Updated pytket version requirement to 1.34.
 * Now supporting ``ClExpr`` operations (the new version of tket's ``ClassicalExpBox``).
+* Updated pytket version requirement to 1.38.0.
 
 0.10.0 (October 2024)
 ---------------------
@@ -41,7 +41,7 @@ Unreleased
 0.7.1 (July 2024)
 -----------------
 
-* New official `documentation site <https://tket.quantinuum.com/extensions/pytket-cutensornet/>`_.
+* New official `documentation site <https://docs.quantinuum.com/tket/extensions/pytket-cutensornet/>`_.
 * Backend methods can now be given a ``scratch_fraction`` argument to configure the amount of GPU memory allocated to cuTensorNet contraction. Users can also configure the values of the ``StateAttribute`` and ``SamplerAttribute`` from cuTensornet via the backend interface.
 * Fixed a bug causing the logger to fail displaying device properties.
 

docs/examples/python/mps_tutorial.py

@@ -15,7 +15,7 @@
 )
 
 # ## Introduction
-# This notebook provides examples of the usage of the MPS functionalities of `pytket_cutensornet`. For more information, see the docs at https://tket.quantinuum.com/extensions/pytket-cutensornet/api/index.html.
+# This notebook provides examples of the usage of the MPS functionalities of `pytket_cutensornet`. For more information, see the docs at https://docs.quantinuum.com/tket/extensions/pytket-cutensornet/.
 # A Matrix Product State (MPS) represents a state on `n` qubits as a list of `n` tensors connected in a line as show below:
 # ![MPS](images/mps.png)
 # Each of these circles corresponds to a tensor. We refer to each leg of a tensor as a *bond* and the number of bonds a tensor has is its *rank*. In code, a tensor is just a multidimensional array:

docs/examples/python/ttn_tutorial.py

@@ -15,7 +15,7 @@
 )
 
 # ## Introduction
-# This notebook provides examples of the usage of the TTN functionalities of `pytket_cutensornet`. For more information, see the docs at https://tket.quantinuum.com/extensions/pytket-cutensornet/api/index.html.
+# This notebook provides examples of the usage of the TTN functionalities of `pytket_cutensornet`. For more information, see the docs at https://docs.quantinuum.com/tket/extensions/pytket-cutensornet/.
 # Some good references to learn about Tree Tensor Network state simulation:
 # - For an introduction into TTN based simulation of quantum circuits: https://arxiv.org/abs/2206.01000
 # - For an introduction on some of the optimisation concerns that are relevant to TTN: https://arxiv.org/abs/2209.03196

docs/index.rst

@@ -11,26 +11,29 @@ a high-performance library aimed at quantum circuit simulations on the NVIDIA GP
 
 We provide two core functionalities:
 
-* *Full tensor network contraction*: use ``tk_to_tensor_network`` to translate a ``pytket`` circuit to a ``TensorNetwork`` and obtain expectation values and amplitudes via full tensor network contraction using ``cuQuantum``'s optimised contraction path.
+* *Exact value calculation*: use ``GeneralState`` and ``GenearlBraOpKet`` to translate a ``pytket`` into a tensor network and obtain amplitudes and expectation values via full tensor network contraction using ``cuQuantum``'s optimised contraction path.
 
-* *Matrix Product State (MPS)*: use ``simulate`` to simulate a ``pytket`` circuit, returning an ``MPS`` representation of the output state, of which you can then ``get_amplitude`` or calculate inner products with other MPS via ``vdot``.
+* *Approximate state evolution*: use ``simulate`` to simulate a ``pytket`` circuit, returning a tensor network representation of the approximate output state, from which you can query properties, such as amplitudes and expectation values.
 
-Currently, only single-GPU calculations are supported, but a multi-GPU execution will be
-implemented in the due course using ``mpi4py`` library.
 
 ``pytket-cutensornet`` is available for Python 3.10, 3.11 and 3.12 on Linux.
-In order to use it, you need access to a Linux machine with an NVIDIA GPU of Compute Capability +7.0 (check it `here <https://developer.nvidia.com/cuda-gpus>`_) and first install ``cuQuantum Python`` following their installation
-`instructions  <https://docs.nvidia.com/cuda/cuquantum/latest/python/README.html#installation>`_.
-This will include the necessary dependencies such as CUDA toolkit. Then, to install
-``pytket-cutensornet``, run:
+In order to use it, you need access to a Linux machine (or WSL) with an NVIDIA GPU of
+Compute Capability +7.0 (check it `here <https://developer.nvidia.com/cuda-gpus>`_).
+You will need to install ``cuda-toolkit`` and ``cuquantum-python`` before ``pytket-cutensornet``;
+for instance, in Ubuntu 24.04:
 
 ::
 
+   sudo apt install cuda-toolkit
+   pip install cuquantum-python
    pip install pytket-cutensornet
 
+Alternatively, you may install cuQuantum Python following their `instructions <https://docs.nvidia.com/cuda/cuquantum/latest/getting-started/index.html>`_
+using ``conda-forge``. This will include the necessary dependencies from CUDA toolkit. Then, you may install
+``pytket-cutensornet`` using ``pip``.
+
 .. toctree::
     api.rst
-    changelog.rst
 
 .. toctree::
    :caption: Example Notebooks
@@ -39,6 +42,11 @@ This will include the necessary dependencies such as CUDA toolkit. Then, to inst
    examples/mps_tutorial.ipynb
    examples/ttn_tutorial.ipynb
 
+.. toctree::
+   :caption: Changelog
+
+   changelog.rst
+
 .. toctree::
    :caption: Useful links
 

lint-requirements.txt

@@ -1,2 +1,3 @@
 black~=24.10
-pylint~=3.3
+pylint~=3.3
+mypy

setup.py

@@ -32,7 +32,7 @@
     author_email="[email protected]",
     python_requires=">=3.10",
     project_urls={
-        "Documentation": "https://tket.quantinuum.com/extensions/pytket-cutensornet/index.html",
+        "Documentation": "https://docs.quantinuum.com/tket/extensions/pytket-cutensornet",
         "Source": "https://github.com/CQCL/pytket-cutensornet",
         "Tracker": "https://github.com/CQCL/pytket-cutensornet/issues",
     },
@@ -42,7 +42,10 @@
     license="Apache 2",
     packages=find_namespace_packages(include=["pytket.*"]),
     include_package_data=True,
-    install_requires=["pytket >= 1.34.0", "networkx >= 2.8.8"],
+    install_requires=[
+        "pytket >= 1.38.0",
+        "networkx >= 2.8.8",
+    ],
     classifiers=[
         "Environment :: Console",
         "Programming Language :: Python :: 3.10",

tests/test-requirements.txt

@@ -1,2 +1,2 @@
-pytest
-pytest-lazy-fixture
+pytest==7.0
+pytest-lazy-fixture