MindQuantum

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• MindQuantum
  • What is MindQuantum
  • First experience
    • Build parameterized quantum circuit
    • Train quantum neural network
  • Tutorials
  • API
  • Installation
    • Confirming System Environment Information
    • Install by Source Code
    • Install by pip
      • Install MindSpore
      • Install MindQuantum
  • Verifying Successful Installation
  • Install with Docker
  • Note
  • Quick Start
  • Docs
  • Community
    • Governance
  • Contributing
  • How to cite
  • License

What is MindQuantum

MindQuantum is a general quantum computing 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 machine learning, quantum chemistry simulation, and quantum optimization, which provides an efficient platform for researchers, teachers and students to quickly design and verify quantum machine learning algorithms.

First experience

Build parameterized quantum circuit

The below example shows how to build a parameterized quantum circuit.

from mindquantum import *
import numpy as np
encoder = Circuit().h(0).rx({'a0': 2}, 0).ry('a1', 1)
print(encoder)
print(encoder.get_qs(pr={'a0': np.pi/2, 'a1': np.pi/2}, ket=True))

Then you will get,

q0: ────H───────RX(2*a0)──

q1: ──RY(a1)──────────────

-1/2j¦00⟩
-1/2j¦01⟩
-1/2j¦10⟩
-1/2j¦11⟩

In jupyter notebook, we can just call svg() of any circuit to display the circuit in svg picture (dark and light mode are also supported).

circuit = (qft(range(3)) + BarrierGate(True)).measure_all()
circuit.svg()

Train quantum neural network

ansatz = CPN(encoder.hermitian(), {'a0': 'b0', 'a1': 'b1'})
sim = Simulator('projectq', 2)
ham = Hamiltonian(-QubitOperator('Z0 Z1'))
grad_ops = sim.get_expectation_with_grad(ham,
                                         encoder + ansatz,
                                         encoder_params_name=encoder.params_name,
                                         ansatz_params_name=ansatz.params_name)

import mindspore as ms
ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target="CPU")
net = MQLayer(grad_ops)
encoder_data = ms.Tensor(np.array([[np.pi/2, np.pi/2]]))
opti = ms.nn.Adam(net.trainable_params(), learning_rate=0.1)
train_net = ms.nn.TrainOneStepCell(net, opti)
for i in range(100):
    train_net(encoder_data)
print(dict(zip(ansatz.params_name, net.trainable_params()[0].asnumpy())))

The trained parameters are,

{'b1': 1.5720831, 'b0': 0.006396801}

Tutorials

1. Basic usage

   • Variational Quantum Circuit

2. Variational quantum algorithm

   • Quantum Approximate Optimization Algorithm
   • The Application of Quantum Neural Network in NLP
   • VQE Application in Quantum Chemistry Computing

API

For more API, please refers to MindQuantum API

Installation

Confirming System Environment Information

• The hardware platform should be CPU with avx2 supported.
• Refer to MindQuantum Installation Guide, install MindSpore, version 1.4.0 or later is required.
• See setup.py for the remaining dependencies.

Install by Source Code

1.Download Source Code from Gitee

cd ~
git clone https://gitee.com/mindspore/mindquantum.git

2.Compiling MindQuantum

cd ~/mindquantum
bash build.sh
cd output
pip install mindquantum-*.whl

Install by pip

Install MindSpore

Please refers to MindSpore installation guide to install MindSpore that at least 1.4.0 version.

Install MindQuantum

• Linux

pip install https://hiq.huaweicloud.com/download/mindquantum/newest/linux/mindquantum-master-cp37-cp37m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple

• Windows

pip install https://hiq.huaweicloud.com/download/mindquantum/newest/windows/mindquantum-master-cp37-cp37m-win_amd64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple

• MacOSX

pip install https://hiq.huaweicloud.com/download/mindquantum/newest/macosx/mindquantum-master-cp37-cp37m-macosx_10_13_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple

• Change cp37-cp37m to cp38-cp38 or cp39-cp39 according to your python version.
• 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.

Verifying Successful Installation

Successfully installed, if there is no error message such as No module named 'mindquantum' when execute the following command:

python -c 'import mindquantum'

Install with Docker

Mac or Windows users can install MindQuantum through Docker. Please refer to Docker installation guide

Note

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:

export OMP_NUM_THREADS=4

For large servers, please set the number of parallel kernels appropriately according to the size of the model to achieve optimal results.

Building binary wheels

If you would like to build some binary wheels for redistribution, please have a look to our binary wheel building guide

Quick Start

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.

Docs

More details about installation guide, tutorials and APIs, please see the User Documentation.

Community

Governance

Check out how MindSpore Open Governance works.

Contributing

Welcome contributions. See our Contributor Wiki for more details.

How to cite

When using MindQuantum for research, please cite:

@misc{mq_2021,
    author      = {MindQuantum Developer},
    title       = {MindQuantum, version 0.5.0},
    month       = {March},
    year        = {2021},
    url         = {https://gitee.com/mindspore/mindquantum}
}

License

Apache License 2.0