fix urls for tql org

Author: refraction-ray

.github/workflows/ci.yml

@@ -6,7 +6,7 @@ jobs:
     name: test
     strategy:
       matrix:
-        os: [ubuntu-18.04] # macos-latest disabled to save quota
+        os: [ubuntu-18.04, macos-latest] # macos-latest disabled to save quota
         python-version: [3.8]
       fail-fast: false
     steps:

README.md

@@ -6,8 +6,8 @@
 
 <p align="center">
   <!-- tests (GitHub actions) -->
-  <a href="https://github.com/quclub/tensorcircuit-dev/actions/workflows/ci.yml">
-    <img src="https://img.shields.io/github/workflow/status/quclub/tensorcircuit-dev/ci/master?logo=github&style=flat-square&logo=github" />
+  <a href="https://github.com/tencent-quantum-lab/tensorcircuit/actions/workflows/ci.yml">
+    <img src="https://img.shields.io/github/workflow/status/tencent-quantum-lab/tensorcircuit/ci/master?logo=github&style=flat-square&logo=github" />
   </a>
   <!-- docs -->
   <a href="">
@@ -84,21 +84,21 @@ We also have [Docker support](/docker).
 
 ## Advantages
 
-* Tensor network simulation engine based
+- Tensor network simulation engine based
 
-* JIT, AD, vectorized parallelism compatible, GPU support
+- JIT, AD, vectorized parallelism compatible, GPU support
 
-* Efficiency
+- Efficiency
 
-  * Time: 10 to 10^6 times acceleration compared to tfq or qiskit
+  - Time: 10 to 10^6 times acceleration compared to tfq or qiskit
 
-  * Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
+  - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
 
-* Elegance
+- Elegance
 
-  * Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions
+  - Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions
 
-  * API design: quantum for humans, less code, more power
+  - API design: quantum for humans, less code, more power
 
 ## Contributing
 
@@ -120,6 +120,5 @@ Reference paper: https://arxiv.org/pdf/2106.05105.pdf and https://arxiv.org/pdf/
 
 ### VQEX - MBL
 
-For the application of VQEX on MBL phase identification, see the [tutorial](https://github.com/quclub/tensorcircuit-tutorials/blob/master/tutorials/vqex_mbl.ipynb).
+For the application of VQEX on MBL phase identification, see the [tutorial](/docs/source/tutorials/vqex_mbl.ipynb).
 Reference paper: https://arxiv.org/pdf/2111.13719.pdf.
-

README_cn.md

@@ -2,8 +2,8 @@
 
 <p align="center">
   <!-- tests (GitHub actions) -->
-  <a href="https://github.com/quclub/tensorcircuit-dev/actions/workflows/ci.yml">
-    <img src="https://img.shields.io/github/workflow/status/quclub/tensorcircuit-dev/ci/master?logo=github&style=flat-square&logo=github" />
+  <a href="https://github.com/tencent-quantum-lab/tensorcircuit/actions/workflows/ci.yml">
+    <img src="https://img.shields.io/github/workflow/status/tencent-quantum-lab/tensorcircuit/ci/master?logo=github&style=flat-square&logo=github" />
   </a>
   <!-- docs -->
   <a href="">
@@ -116,5 +116,5 @@ pip install tensorcircuit
 
 ### VQEX - MBL
 
-VQEX 在 MBL 相位识别上的应用见 [教程](https://github.com/quclub/tensorcircuit-tutorials/blob/master/tutorials/vqex_mbl.ipynb)。
+VQEX 在 MBL 相位识别上的应用见 [教程](/docs/source/tutorials/vqex_mbl.ipynb)。
 参考论文: https://arxiv.org/pdf/2111.13719.pdf 。

docs/source/contribs/development_windows.rst

@@ -3,7 +3,7 @@ Run TensorCircuit on Windows Machine with Docker
 
 Contributed by `SexyCarrots <https://github.com/SexyCarrots>`_ (Xinghan Yang)
 
-(For linux machines, please review the `Docker README for linux <https://github.com/quclub/tensorcircuit-dev/blob/master/docker/README.md>`_ )
+(For linux machines, please review the `Docker README for linux <https://github.com/tencent-quantum-lab/tensorcircuit/blob/master/docker/README.md>`_ )
 
 This note is only a step-by-step tutorial to help you build and run a Docker Container for Windows Machine users with the given dockerfile. 
 If you want to have a deeper dive in to Docker, please check the official `Docker Orientation <https://docs.docker.com/get-started/>`_
@@ -78,11 +78,11 @@ Build Image in through PyCharm or Command Line Interface
 
 **For CLI command:**
 
-Go to your local ``./tensorcircuit-dev/docker`` directory, then open your local CLI.
+Go to your local ``./tensorcircuit/docker`` directory, then open your local CLI.
 
 .. code-block:: bash
 
-    cd ./tensorcircuit-dev/docker
+    cd ./tensorcircuit/docker
 
 Use the command:
 
@@ -96,7 +96,7 @@ there is any interruption during the building.
 
 **For PyCharm:**
 
-Install the docker plugin within Pycharm, than open the dockerfile in the ``./tensorcircuit-dev/docker`` directory.
+Install the docker plugin within Pycharm, than open the dockerfile in the ``./tensorcircuit/docker`` directory.
 Choose Dockerfile to be the configuration, then run the dockerfile.
 Please keep your computer active while building the docker image. You need to build the image again from scratch if
 there is any interruption during the building.
@@ -130,7 +130,7 @@ Then, open docker desktop and open docker CLI:
 
     ls
 
-You would see all files and directories in ``./tensorcircuit-dev/`` listed.
+You would see all files and directories in ``./tensorcircuit/`` listed.
 
 Go to the dir where all examples are:
 

docs/source/contribution.rst

@@ -1,7 +1,7 @@
 Guide for Contributors
 ============================
 
-We welcome everyone’s contributions! The development of TensorCircuit is open-sourced and centered on `GitHub <https://github.com/quclub/tensorcircuit-dev>`_.
+We welcome everyone’s contributions! The development of TensorCircuit is open-sourced and centered on `GitHub <https://github.com/tencent-quantum-lab/tensorcircuit>`_.
 
 There are various ways to contribute:
 

docs/source/quickstart.rst

@@ -138,7 +138,7 @@ The most common case and the most typical programming paradigm for TensorCircuit
     params = K.implicit_randn([2, n])
     print(vgf(params, n))  # get the quantum loss and the gradient
 
-Also for a non-quantum example (linear regression) demonstrating the backend agnostic feature, variables with pytree support, AD/jit/vmap usage, and variational optimization loops. Please refer to the example script: `linear regression example <https://github.com/quclub/tensorcircuit-dev/blob/master/examples/universal_lr.py>`_.
+Also for a non-quantum example (linear regression) demonstrating the backend agnostic feature, variables with pytree support, AD/jit/vmap usage, and variational optimization loops. Please refer to the example script: `linear regression example <https://github.com/tencent-quantum-lab/tensorcircuit/blob/master/examples/universal_lr.py>`_.
 This example might be more friendly to the machine learning community since it is purely classical while also showcasing the main features and paradigms of tensorcircuit.
 
 If the user has no intention to maintain the application code in a backend agnostic fashion, the API for ML frameworks can be more handily used and interleaved with the TensorCircuit API.
@@ -411,7 +411,7 @@ Some setup cases:
     # 3. state simulator like contractor provided by tensorcircuit, maybe better when there is ring topology for two-qubit gate layout
     tc.set_contractor("plain-experimental")
 
-For advanced configurations on cotengra contractors, please refer to cotengra `doc <https://cotengra.readthedocs.io/en/latest/advanced.html>`__ and more fancy examples can be found at `contractor tutorial <https://github.com/quclub/tensorcircuit-tutorials/blob/master/tutorials/contractors.ipynb>`__.
+For advanced configurations on cotengra contractors, please refer to cotengra `doc <https://cotengra.readthedocs.io/en/latest/advanced.html>`__ and more fancy examples can be found at `contractor tutorial <https://github.com/tencent-quantum-lab/tensorcircuit-tutorials/blob/master/tutorials/contractors.ipynb>`__.
 
 **Setup in Function or Context Level**