MindQuantum Documents

MindQuantum is a new-generation quantum computing suite based on MindSpore, supporting training and inference of multiple quantum neural networks.

MindQuantum focuses 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.

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 and Long algorithms 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