MindQuantum Documents

MindQuantum is a general-purpose quantum computing library designed to train and infer multiple quantum neural networks. Developed by MindSpore and HiQ, it leverages the quantum computing simulator developed by HiQ and high-performance automatic differentiation of MindSpore, ensuring MindQuantum can efficiently solve problems in quantum machine learning, chemistry simulation, and optimization. It provides a platform for researchers, teachers, and students to quickly design and verify quantum machine learning algorithms.

Typical Application Scenarios

1. Quantum Machine Learning

   Add the quantum neural network to the training process to improve the convergence accuracy.

2. Quantum Chemical Simulation

   Use VQE to solve the ground state energy of molecular system.

3. Quantum Combinatorial Optimization

   Use QAOA to solve the Max-Cut problem.

Installation

• MindQuantum Installation

Guide

• Variational Quantum Circuit
• Initial experience of quantum neural network
• Advanced gradient calculation of variational quantum circuits

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

General Quantum Algorithm

• Quantum Phase Estimation Algorithm
• Grover search algorithm based on MindQuantum
• Shor’s algorithm based on MindQuantum

API References

• mindquantum.core
• mindquantum.simulator
• mindquantum.framework
• mindquantum.algorithm
• mindquantum.io
• mindquantum.engine
• mindquantum.utils

RELEASE NOTES

• Release Notes