• 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
    • Build from source
  • Verifying Successful Installation
  • Install with Docker
  • Note
  • Building binary wheels
  • Quick Start
  • Docs
  • Community
  • • Governance
  • Contributing
  • How to cite
  • License

MindQuantum

查看中文

• 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 new-generation quantum computing framework based on MindSpore, focusing on the implementation of NISQ algorithms. It combines the HiQ high-performance quantum computing simulator with the parallel automatic differentiation capability of MindSpore. MindQuantum is easy-to-use with ultra-high performance. It can efficiently handle problems like quantum machine learning, quantum chemistry simulation, and quantum optimization. MindQuantum provides an efficient platform for researchers, teachers and students to quickly design and verify quantum algorithms, making quantum computing at your fingertips.

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()  # circuit.svg('light')

Train quantum neural network

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

import mindspore as ms

ms.set_context(mode=ms.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
   • Quantum simulator
   • Initial experience of quantum neural network
   • Advanced gradient calculation of variational quantum circuits
   • Advanced operations of quantum circuit
   • Quantum Measurement
   • Noisy Quantum Circuit
   • Noise simulator
   • Qubit Mapping
   • Bloch Sphere

2. Variational quantum algorithm

   • Classification of iris by quantum neural network
   • Quantum Approximate Optimization Algorithm
   • The Application of Quantum Neural Network in NLP
   • VQE Application in Quantum Chemistry Computing
   • Equivalence Checking of Parameterized Quantum Circuits

3. GENERAL QUANTUM ALGORITHM

   • Quantum Phase Estimation algorithm
   • Grover search and long algorithms based on MindQuantum
   • Shor’s algorithm based on MindQuantum

API

For more API, please refer 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 refer to MindSpore installation guide to install MindSpore that at least 1.4.0 version.

Install MindQuantum

pip install mindquantum

Build from source

1. Clone source.

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

2. Build MindQuantum

   For linux system, please make sure your cmake version >= 3.18.3, and then run code:

   cd ~/mindquantum
   bash build.sh --gitee
   

   Here --gitee is telling the script to download third party from gitee. If you want to download from github, you can ignore this flag. If you want to build under GPU, please make sure you have install CUDA 11.x and the GPU driver, and then run code:

   cd ~/mindquantum
   bash build.sh --gitee --gpu
   

   For windows system, please make sure you have install MinGW-W64 and CMake >= 3.18.3, and then run:

   cd ~/mindquantum
   ./build.bat /Gitee
   

   For Mac system, please make sure you have install openmp and CMake >= 3.18.3, and then run:

   cd ~/mindquantum
   bash build.sh --gitee
   

3. Install whl

   Please go to output, and install mindquantum wheel package by pip.

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 0.6.0},
    month       = {March},
    year        = {2021},
    url         = {https://gitee.com/mindspore/mindquantum}
}

License

Apache License 2.0