updating github orgname

README.md

@@ -1,7 +1,7 @@
 ![](doc/source/qibo_logo.svg)
 
-![Tests](https://github.com/Quantum-TII/qibo/workflows/Tests/badge.svg)
-[![codecov](https://codecov.io/gh/Quantum-TII/qibo/branch/master/graph/badge.svg?token=1EKZKVEVX0)](https://codecov.io/gh/Quantum-TII/qibo)
+![Tests](https://github.com/qiboteam/qibo/workflows/Tests/badge.svg)
+[![codecov](https://codecov.io/gh/qiboteam/qibo/branch/master/graph/badge.svg?token=1EKZKVEVX0)](https://codecov.io/gh/qiboteam/qibo)
 [![Documentation Status](https://readthedocs.org/projects/qibo/badge/?version=latest)](https://qibo.readthedocs.io/en/latest/?badge=latest)
 [![DOI](https://zenodo.org/badge/241307936.svg)](https://zenodo.org/badge/latestdoi/241307936)
 

doc/source/benchmarks.rst

@@ -30,7 +30,7 @@ respective default simulation precision and supported hardware configurations.
    * - Library
      - Precision
      - Hardware
-   * - `Qibo 0.1.0 <https://github.com/Quantum-TII/qibo>`_
+   * - `Qibo 0.1.0 <https://github.com/qiboteam/qibo>`_
      - single/double
      - multi-thread CPU, GPU, multi-GPU
    * - `Cirq 0.8.1 <https://github.com/quantumlib/Cirq>`_
@@ -69,7 +69,7 @@ The operating system of this machine is the default Ubuntu 18.04-LTS with
 CUDA/``nvcc 10.1``, TensorFlow 2.2.0 and ``g++ 7.5``.
 
 The following sections describe how to run Qibo benchmarks using the scripts
-found at: https://github.com/Quantum-TII/qibo/tree/master/examples/benchmarks.
+found at: https://github.com/qiboteam/qibo/tree/master/examples/benchmarks.
 
 
 How to run circuit benchmarks?

doc/source/installation.rst

@@ -130,7 +130,7 @@ In order to install Qibo from source, you can simply clone the GitHub repository
 
 .. code-block::
 
-      git clone https://github.com/Quantum-TII/qibo.git
+      git clone https://github.com/qiboteam/qibo.git
       cd qibo
       pip install . # or pip install -e .
 
@@ -188,7 +188,7 @@ In order to install the package perform the following steps:
 
 .. code-block::
 
-      git clone https://github.com/Quantum-TII/qibotf.git
+      git clone https://github.com/qiboteam/qibotf.git
       cd qibotf
 
 then proceed with the installation of requirements with:

examples/3_tangle/README.md

@@ -1,6 +1,6 @@
 # Measuring the tangle of three-qubit states
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/3_tangle](https://github.com/Quantum-TII/qibo/tree/master/examples/3_tangle).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/3_tangle](https://github.com/qiboteam/qibo/tree/master/examples/3_tangle).
 
 Based in the paper [Entropy 2020, 22(4), 436](http://dx.doi.org/10.3390/e22040436). In this `README.md` file you can see a short
 set of instructions for the usage of the example and brief explanations.

examples/EF_QAE/README.md

@@ -1,6 +1,6 @@
 # Quantum autoencoders with enhanced data encoding
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/EF_QAE](https://github.com/Quantum-TII/qibo/tree/master/examples/EF_QAE).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/EF_QAE](https://github.com/qiboteam/qibo/tree/master/examples/EF_QAE).
 
 ## Problem overview
 
@@ -10,7 +10,7 @@ Remarkably, EF-QAE achieves better compression than the standard [quantum autoen
 
 ## Implementing the solution
 
-The code herein aims to implement the EF-QAE, based on the manuscript ["Quantum autoencoders with enhanced data encoding"](https://arxiv.org/abs/2010.06599). A tutorial based on the standard QAE can be found at [https://github.com/Quantum-TII/qibo/tree/master/examples/autoencoder](https://github.com/Quantum-TII/qibo/tree/master/examples/autoencoder).
+The code herein aims to implement the EF-QAE, based on the manuscript ["Quantum autoencoders with enhanced data encoding"](https://arxiv.org/abs/2010.06599). A tutorial based on the standard QAE can be found at [https://github.com/qiboteam/qibo/tree/master/examples/autoencoder](https://github.com/qiboteam/qibo/tree/master/examples/autoencoder).
 
 A graphical depiction of a quantum encoder can be seen in the following figure. In a quantum encoder the information contained in some of the input qubits must be discarded after the initial encoding. Then, fresh qubits (here initialized to the |0> state, but one may consider any other easy-to-construct reference state) are prepared and used to implement the final decoding, which is finally compared to the initial state.
 

examples/aavqe/README.md

@@ -1,6 +1,6 @@
 # Scaling of variational quantum circuit depth for condensed matter systems
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/aavqe](https://github.com/Quantum-TII/qibo/tree/master/examples/aavqe).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/aavqe](https://github.com/qiboteam/qibo/tree/master/examples/aavqe).
 
 ## Problem overview
 Strongly-correlated many-body systems can give rise to exceptional quantum

examples/adiabatic/README.md

@@ -1,6 +1,6 @@
 # Simple Adiabatic Evolution Examples
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/adiabatic](https://github.com/Quantum-TII/qibo/tree/master/examples/adiabatic)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/adiabatic](https://github.com/qiboteam/qibo/tree/master/examples/adiabatic)
 
 ## Introduction
 

examples/adiabatic3sat/README.md

@@ -1,6 +1,6 @@
 # Adiabatic evolution for solving an Exact Cover problem
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/adiabatic3sat](https://github.com/Quantum-TII/qibo/tree/master/examples/adiabatic3sat)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/adiabatic3sat](https://github.com/qiboteam/qibo/tree/master/examples/adiabatic3sat)
 
 ## Introduction
 

examples/autoencoder/README.md

@@ -1,6 +1,6 @@
 # Quantum autoencoder for data compression
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/autoencoder](https://github.com/Quantum-TII/qibo/tree/master/examples/autoencoder).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/autoencoder](https://github.com/qiboteam/qibo/tree/master/examples/autoencoder).
 
 ## Problem overview
 

examples/bell-variational/README.md

@@ -1,6 +1,6 @@
 # Maximal violation of Bell inequalities variationally
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/bell-variational](https://github.com/Quantum-TII/qibo/tree/master/examples/bell-variational)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/bell-variational](https://github.com/qiboteam/qibo/tree/master/examples/bell-variational)
 
 ## Introduction
 

examples/falqon/README.md

@@ -1,6 +1,6 @@
 # Feedback-based ALgorithm for Quantum OptimizatioN - FALQON
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/falqon](https://github.com/Quantum-TII/qibo/tree/master/examples/falqon)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/falqon](https://github.com/qiboteam/qibo/tree/master/examples/falqon)
 
 Quantum Approximate Optimisation Algorithm (QAOA) is considered as one of the most important algorithms for optimisation in Quantum Computers, see [arXiv:1411.4028](https://arxiv.org/abs/1411.4028) by Farhi, Goldstone and Gutmann for more information.
 

examples/grover/README.md

@@ -1,6 +1,6 @@
 # A General Grover Model
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/grover](https://github.com/Quantum-TII/qibo/tree/master/examples/grover).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/grover](https://github.com/qiboteam/qibo/tree/master/examples/grover).
 
 The examples presented here provide information to run a general Grover model accessible as
 `from qibo.models import Grover`. This model allows to construct a general

examples/grover3sat/README.md

@@ -1,6 +1,6 @@
 # Grover's Algorithm for solving Satisfiability Problems
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/grover3sat](https://github.com/Quantum-TII/qibo/tree/master/examples/grover3sat)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/grover3sat](https://github.com/qiboteam/qibo/tree/master/examples/grover3sat)
 
 ## Introduction
 

examples/hash-grover/README.md

@@ -1,6 +1,6 @@
 # Grover's Algorithm for solving a Toy Sponge Hash function
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/hash-grover](https://github.com/Quantum-TII/qibo/tree/master/examples/hash-grover)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/hash-grover](https://github.com/qiboteam/qibo/tree/master/examples/hash-grover)
 
 ## Introduction
 

examples/qPDF/qPDF.ipynb

@@ -14,7 +14,7 @@
    "source": [
     "# Determining the proton content with a quantum computer\n",
     "\n",
-    "Code at: https://github.com/Quantum-TII/qibo/tree/master/examples/qPDF.\n",
+    "Code at: https://github.com/qiboteam/qibo/tree/master/examples/qPDF.\n",
     "\n",
     "In this tutorial we show how to use the `qPDF` model implemented in Qibo to create a set of Parton Distribution Functions (PDFs), parameterized by a variational quantum circuit. In the context of High Energy Physics, parton distribution functions estimate the momentum fraction of the proton carried by partons i.e. quarks, antiquarks and gluon. Here we simulate a quantum computer to encode within a circuit the data from these PDFs in such a way that, if we measure the output of the aforementioned quantum circuit, we obtain the corresponding PDFs values.\n",
     "\n",

examples/qsvd/README.md

@@ -1,6 +1,6 @@
 # Quantum Singular Value Decomposer
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/qsvd](https://github.com/Quantum-TII/qibo/tree/master/examples/qsvd)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/qsvd](https://github.com/qiboteam/qibo/tree/master/examples/qsvd)
 
 ## Problem overview
 Much progress has been made towards a better understanding of bipartite and multipartite entanglement of quantum systems in the last decades. Among the many figures of merit that have been put forward to quantify entanglement, the von Neumann entropy stands out as it finely reveals the quantum correlations between subparts of the system. Yet, the explicit computation of this entropy, as well as many other bipartite measures of entanglement, relies on a clever decomposition of the tensor that describes a two-party system, and in general, it demands a large investment of computational resources.

examples/reuploading_classifier/README.md

@@ -1,6 +1,6 @@
 # Data reuploading for a universal quantum classifier
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/reuploading_classifier](https://github.com/Quantum-TII/qibo/tree/master/examples/reuploading_classifier).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/reuploading_classifier](https://github.com/qiboteam/qibo/tree/master/examples/reuploading_classifier).
 
 
 Based in the paper [Quantum 4, 226 (2020).](https://quantum-journal.org/papers/q-2020-02-06-226/). In this `README.md` file you can see a short

examples/shor/README.md

@@ -1,6 +1,6 @@
 # Shor's factorization algorithm
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/shor/examples/shor](https://github.com/Quantum-TII/qibo/tree/shor/examples/shor)
+Code at: [https://github.com/qiboteam/qibo/tree/shor/examples/shor](https://github.com/qiboteam/qibo/tree/shor/examples/shor)
 
 The original paper by Shor [arXiv:9508027](https://arxiv.org/abs/quant-ph/9508027) stated that the discrete logarithm problem and factorization could be reduced to order finding. Then, Shor proposed an order finding algorithm using a quantum computer that would run in polynomial time. 
 

examples/unary/README.md

@@ -1,6 +1,6 @@
 # Quantum unary approach to option pricing
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/unary](https://github.com/Quantum-TII/qibo/tree/master/examples/unary)
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/unary](https://github.com/qiboteam/qibo/tree/master/examples/unary)
 
 Based in the paper [arXiv:1912.01618](https://arxiv.org/abs/1912.01618). In this `README.md` file you can see a short
 set of instructions for the usage of the example.

examples/variational_classifier/README.md

@@ -1,6 +1,6 @@
 # Variational Quantum Classifier
 
-Code at: [https://github.com/Quantum-TII/qibo/tree/master/examples/variational_classifier](https://github.com/Quantum-TII/qibo/tree/master/examples/variational_classifier).
+Code at: [https://github.com/qiboteam/qibo/tree/master/examples/variational_classifier](https://github.com/qiboteam/qibo/tree/master/examples/variational_classifier).
 
 ## Problem overview
 

setup.py

@@ -46,7 +46,7 @@ def get_version():
     description="A framework for quantum computing with hardware acceleration.",
     author="The Qibo team",
     author_email="",
-    url="https://github.com/Quantum-TII/qibo",
+    url="https://github.com/qiboteam/qibo",
     packages=find_packages("src"),
     package_dir={"": "src"},
     package_data={"": ["*.out"]},