diff --git a/README.md b/README.md index 58161dfa6..ecef2d7ab 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ [![Total downloads](https://static.pepy.tech/badge/qiskit-machine-learning)](https://pepy.tech/project/qiskit-machine-learning) [![Slack Organisation](https://img.shields.io/badge/slack-chat-blueviolet.svg?label=Qiskit%20Slack&logo=slack)](https://slack.qiskit.org) [![arXiv](https://img.shields.io/badge/arXiv-2505.17756-b31b1b.svg)](https://arxiv.org/abs/2505.17756) +[![arXiv](https://img.shields.io/badge/GitHub%20Pages-Documentation-blue.svg)](https://qiskit-community.github.io/qiskit-machine-learning/) @@ -18,14 +19,15 @@ Qiskit Machine Learning introduces fundamental computational building blocks, su Kernels and Quantum Neural Networks, used in various applications including classification and regression. -This library is part of the Qiskit Community ecosystem, a collection of high-level codes that are based +This library is part of the Qiskit Community ecosystem, a collection of high-level libraries that are based on the Qiskit software development kit. As of version `0.7`, Qiskit Machine Learning is co-maintained by IBM and the [Hartree Centre](https://www.hartree.stfc.ac.uk/), part of the UK Science and Technologies Facilities Council (STFC). > [!NOTE] > A description of the library structure, features, and domain-specific applications, can be found -> in a dedicated [ArXiv paper](https://arxiv.org/abs/2505.17756). +> in a dedicated [![arXiv](https://img.shields.io/badge/arXiv-2505.17756-b31b1b.svg)](https://arxiv.org/abs/2505.17756) +> paper. For more details on usage and the API, refer to the [![arXiv](https://img.shields.io/badge/Documentation-blue.svg)](https://qiskit-community.github.io/qiskit-machine-learning/). The Qiskit Machine Learning framework aims to be: @@ -68,7 +70,8 @@ The [`TorchConnector`](https://qiskit-community.github.io/qiskit-machine-learnin integrates QNNs with [PyTorch](https://pytorch.org). Thanks to the gradient algorithms in Qiskit Machine Learning, this includes automatic differentiation. The overall gradients computed by PyTorch during the backpropagation take into account quantum neural -networks, too. The flexible design also allows the building of connectors to other packages in the future. +networks, too. The flexible design also allows the building of connectors to other packages or accelerated +libraries. ## Installation and documentation @@ -90,33 +93,25 @@ For more details on how to do so and much more, follow the instructions in the ### Optional Installs * **PyTorch** may be installed either using command `pip install 'qiskit-machine-learning[torch]'` to install the - package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/). When PyTorch - is installed, the `TorchConnector` facilitates its use of quantum computed networks. + package or refer to PyTorch [getting started](https://pytorch.org/get-started/locally/) guide. When PyTorch + is installed, the `TorchConnector` facilitates the combination of hybrid quantum-classical neural networks. * **Sparse** may be installed using command `pip install 'qiskit-machine-learning[sparse]'` to install the - package. Sparse being installed will enable the usage of sparse arrays and tensors. + package. [Sparse](https://sparse.pydata.org/en/latest/) is built on top of NumPy and `scipy.sparse`, and enables + efficient operations of sparse arrays and tensors. Refer to the Sparse [installation guide](https://sparse.pydata.org/en/latest/install/) + for further details. * **NLopt** is required for the global optimizers. [`NLopt`](https://nlopt.readthedocs.io/en/latest/) can be installed manually with `pip install nlopt` on Windows and Linux platforms, or with `brew install nlopt` on MacOS using the Homebrew package manager. For more information, refer to the [installation guide](https://nlopt.readthedocs.io/en/latest/NLopt_Installation/). -## Migration to Qiskit 1.x -> [!NOTE] -> Qiskit Machine Learning depends on Qiskit, which will be automatically installed as a -> dependency when you install Qiskit Machine Learning. From version `0.8` of Qiskit Machine -> Learning, Qiskit `1.0` or above will be required. If you have a pre-`1.0` version of Qiskit -> installed in your environment (however it was installed), you should upgrade to `1.x` to -> continue using the latest features. You may refer to the -> official [Qiskit 1.0 Migration Guide](https://quantum.cloud.ibm.com/docs/migration-guides/qiskit-1.0) -> for detailed instructions and examples on how to upgrade Qiskit. - ---------------------------------------------------------------------------------------------------- -### Creating Your First Machine Learning Programming Experiment in Qiskit +### Creating your first Qiskit Machine Learning program -Now that Qiskit Machine Learning is installed, it's time to begin working with the Machine -Learning module. Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to +Now that Qiskit Machine Learning is installed, it's time to begin working with the machine +learning modules. Let's try an experiment using VQC (Variational Quantum Classifier) algorithm to train and test samples from a data set to see how accurately the test set can be classified. ```python @@ -156,18 +151,20 @@ print(f"Testing accuracy: {score:0.2f}") ### More examples -Learning path notebooks may be found in the -[Machine Learning tutorials](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html) section -of the documentation and are a great place to start. +Learning materials can be found in the +[Tutorials](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html) section +of the documentation. These notebooks will walk you step by step through different tasks and are designed to be hackable, +making them a great place to start. Another good place to learn the fundamentals of quantum machine learning is the -[Quantum Machine Learning](https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning#) notebooks from the original Qiskit Textbook. The notebooks are convenient for beginners who are eager to learn +[Quantum Machine Learning](https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning#) notebooks from the original Qiskit Textbook (now archived). +The notebooks are convenient for beginners who are eager to learn quantum machine learning from scratch, as well as understand the background and theory behind algorithms in -Qiskit Machine Learning. The notebooks cover a variety of topics to build understanding of parameterized -circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine -learning - how to build and train quantum ML models for supervised and unsupervised learning. -The Textbook notebooks are complementary to the tutorials of this module; whereas these tutorials focus -on actual Qiskit Machine Learning algorithms, the Textbook notebooks more explain and detail underlying fundamentals +Qiskit Machine Learning. The notebooks cover a variety of topics to build an understanding of parameterized +circuits, data encoding, variational algorithms and more, with the ultimate goal of building and training quantum ML models +for supervised and unsupervised learning. +The Textbook notebooks are complementary to the tutorials of this library. These tutorials focus emphasize the algorithms, +while the Textbook notebooks explain in more detail the underlying fundamental quantum information principles of quantum machine learning. ---------------------------------------------------------------------------------------------------- @@ -185,6 +182,12 @@ and use the [`#qiskit-machine-learning`](https://qiskit.enterprise.slack.com/arc channel for discussions and short questions. For questions that are more suited for a forum, you can use the **Qiskit** tag in [Stack Overflow](https://stackoverflow.com/questions/tagged/qiskit). +## How can I cite Qiskit Machine Learning? + +If you use Qiskit Machine Learning in your work, please cite the "overview" [ArXiv paper](https://arxiv.org/abs/2505.17756) to +support the continued development and visibility of the library. The BibTeX citation handle can be found in the +[`CITATION.bib`](./CITATION.bib) file. + ## Humans behind Qiskit Machine Learning Qiskit Machine Learning was inspired, authored and brought about by the collective work of a @@ -193,11 +196,6 @@ work of [many people](https://github.com/qiskit-community/qiskit-machine-learning/graphs/contributors), who contribute to the project at different levels. -## How can I cite Qiskit Machine Learning? -If you use Qiskit Machine Learning in your work, please cite the "overview" paper to -support the continued development and visibility of the library. The BibTeX citation handle can be found in the -[`CITATION.bib`](./CITATION.bib) file. - ## License This project uses the [Apache License 2.0](https://github.com/qiskit-community/qiskit-machine-learning/blob/main/LICENSE.txt). diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 709651035..88dbfd778 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -102,20 +102,30 @@ Optional installs ``brew install nlopt`` on MacOS using the Homebrew package manager. For more information, refer to the `installation guide `__. -.. _migration-to-qiskit-1x: -Migration to Qiskit 1.x -======================== +.. _contributing: -.. note:: +How can I contribute? +===================== - Qiskit Machine Learning depends on Qiskit, which will be automatically installed as a - dependency when you install Qiskit Machine Learning. From version ``0.8`` of Qiskit Machine - Learning, Qiskit ``1.0`` or above will be required. If you have a pre-``1.0`` version of Qiskit - installed in your environment (however it was installed), you should upgrade to ``1.x`` to - continue using the latest features. You may refer to the - official `Qiskit 1.0 Migration Guide `_ - for detailed instructions and examples on how to upgrade Qiskit. +If you'd like to contribute to Qiskit, please take a look at our +`contribution guidelines `_. +This project adheres to the Qiskit `code of conduct `_. +By participating, you are expected to uphold this code. + +We use `GitHub issues `_ for tracking requests and bugs. Please +`join the Qiskit Slack community `_ +and use the `#qiskit-machine-learning `_ +channel for discussions and short questions. +For questions that are more suited for a forum, you can use the **Qiskit** tag in `Stack Overflow `_. + +.. _citing: + +How can I cite Qiskit Machine Learning? +======================================= + +If you use Qiskit Machine Learning in your work, please cite the "overview" `ArXiv paper `_ to +support the continued development and visibility of the library. ----