Add arXiv and github badges

README.md

@@ -1,20 +1,20 @@
 
 # OQuPy: Open Quantum Systems in Python
 
-[**ArXiv:2406.16650 - A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.**](https://doi.org/10.48550/arXiv.2406.16650)
+[**A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.**](https://doi.org/10.48550/arXiv.2406.16650)
 
+[![ArXiv](https://img.shields.io/badge/arXiv-2406.16650-red)](https://arxiv.org/abs/2406.16650)
+[![Documentation Status](https://readthedocs.org/projects/oqupy/badge/?version=latest)](https://oqupy.readthedocs.io/en/latest/?badge=latest)
+[![DOI](https://www.zenodo.org/badge/244404030.svg)](https://www.zenodo.org/badge/latestdoi/244404030)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tempoCollaboration/OQuPy/main?filepath=tutorials%2Fquickstart.ipynb)
 [![Tests status](https://github.com/tempoCollaboration/OQuPy/actions/workflows/python-package-tests.yml/badge.svg)](https://github.com/tempoCollaboration/OQuPy/actions/workflows/python-package-tests.yml)
 [![Codecov](https://codecov.io/gh/tempoCollaboration/OQuPy/branch/main/graph/badge.svg)](https://codecov.io/gh/tempoCollaboration/OQuPy)
-[![Documentation Status](https://readthedocs.org/projects/oqupy/badge/?version=latest)](https://oqupy.readthedocs.io/en/latest/?badge=latest)
 [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](https://github.com/tempoCollaboration/OQuPy/blob/main/CODE_OF_CONDUCT.md)
-[![DOI](https://www.zenodo.org/badge/244404030.svg)](https://www.zenodo.org/badge/latestdoi/244404030)
 
 [![Unitary Fund](https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge)](http://unitary.fund)
 
 
-
-This open source project aims to facilitate versatile numerical tools to efficiently compute the dynamics of quantum systems that are possibly strongly coupled to structured environments. It facilitates the convenient application of several numerical methods that combine the conceptional advantages of the process tensor framework [1], with the numerical efficiency of tensor networks.
+This open source project aims to facilitate versatile numerical tools to efficiently compute the dynamics of quantum systems that are possibly strongly coupled to structured environments. It offers the convenient application of several numerical methods that combine the conceptional advantages of the process tensor framework [1], with the numerical efficiency of tensor networks.
 
 OQuPy includes numerically exact methods (i.e. employing only numerically well controlled approximations) for the non-Markovian dynamics and multi-time correlations of ...
 - quantum systems coupled to a single environment [2-4],

docs/index.rst

@@ -1,15 +1,21 @@
 OQuPy: Open Quantum Systems in Python
 =====================================
 
-`ArXiv:2406.16650 <https://doi.org/10.48550/arXiv.2406.16650>`__ - **A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.**
-
-.. image:: https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge
- :target: http://unitary.fund
-
+**A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.**
+
+.. image:: https://img.shields.io/badge/arXiv-2406.16650-red
+   :alt: ArXiv
+   :target: https://arxiv.org/abs/2406.16650
+.. image:: https://img.shields.io/badge/GitHub-tempoCollaboration%2FOQuPy-green
+   :alt: GitHub
+   :target: https://github.com/tempoCollaboration/OQuPy
+.. image:: https://www.zenodo.org/badge/244404030.svg
+   :alt: DOI
+   :target: https://www.zenodo.org/badge/latestdoi/244404030
 
 This open source project aims to facilitate versatile numerical tools to
 efficiently compute the dynamics of quantum systems that are possibly
-strongly coupled to structured environments. It facilitates the
+strongly coupled to structured environments. It offers the
 convenient application of several numerical methods that combine the
 conceptional advantages of the process tensor framework [1], with the
 numerical efficiency of tensor networks.
@@ -70,6 +76,9 @@ Furthermore, OQuPy implements methods to ...
 | **Tutorial**       | launch |binder-tutorial|                               |
 +--------------------+--------------------------------------------------------+
 
+.. image:: https://img.shields.io/badge/Supported%20By-UNITARY%20FUND-brightgreen.svg?style=for-the-badge
+ :target: http://unitary.fund
+
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