README: One more link fix (#64)

Author: Yoric

README.md

@@ -3,7 +3,7 @@
 
 The Quantum Evolution Kernel is a Python library designed for the machine learning community to help users design quantum-driven similarity metrics for graphs and to use them inside kernel-based machine learning algorithms for graph data.
 
-The core of the library is focused on the development of a classification algorithm for molecular-graph dataset as it is presented in the published paper [Quantum feature maps for graph machine learning on a neutral atom quantum processor](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615)].
+The core of the library is focused on the development of a classification algorithm for molecular-graph dataset as it is presented in the published paper [Quantum feature maps for graph machine learning on a neutral atom quantum processor](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615).
 
 Users setting their first steps into quantum computing will learn how to implement the core algorithm in a few simple steps and run it using the Pasqal Neutral Atom QPU. More experienced users will find this library to provide the right environment to explore new ideas - both in terms of methodologies and data domain - while always interacting with a simple and intuitive QPU interface.
 
@@ -83,7 +83,7 @@ We have a two parts tutorial:
 1. [Using a Quantum Device to extract machine-learning features](examples/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb);
 2. [Machine Learning with the Quantum Evolution Kernel](examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)
 
-See also the [full API documentation](https://pqs.pages.pasqal.com/quantum-evolution-kernel/).
+See also the [full API documentation](https://pasqal-io.github.io/quantum-evolution-kernel/latest/).
 
 ## Getting in touch