MindQuantum is a quantum machine learning framework developed by MindSpore and HiQ, that can be used to build and train different quantum neural networks. Thanks to the powerful algorithm of quantum software group of Huawei and High-performance automatic differentiation ability of MindSpore, MindQuantum can efficiently handle problems such as quantum chemical simulation and quantum approximation optimization with TOP1 performance, which provides an efficient platform for researchers, teachers and students to quickly design and verify quantum machine learning algorithms.
1.Download Source Code from Gitee
cd ~ git clone https://gitee.com/mindspore/mindquantum.git
cd ~/mindquantum python setup.py install --user
pip install https://hiq.huaweicloud.com/download/mindspore/cpu/x86_64/mindspore-1.3.0-cp38-cp38-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install https://hiq.huaweicloud.com/download/mindquantum/any/mindquantum-0.2.0-py3-none-any.whl -i https://pypi.tuna.tsinghua.edu.cn/simple
- When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about other dependency items, see setup.py). In other cases, you need to manually install dependency items.
Successfully installed, if there is no error message such as No module named 'mindquantum' when execute the following command:
python -c 'import mindquantum'
Mac or Windows users can install MindQuantum through Docker. Please refer to Docker installation guide
Please set the parallel core number before running MindQuantum scripts. For example, if you want to set the parallel core number to 4, please run the command below:
For large servers, please set the number of parallel kernels appropriately according to the size of the model to achieve optimal results.
For more details about how to build a parameterized quantum circuit and a quantum neural network and how to train these models, see the MindQuantum Tutorial.
More details about installation guide, tutorials and APIs, please see the User Documentation.
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