Introduction to Quantum-Train Toolkit

Chen-Yu Liu, Chu-Hsuan Abraham Lin, Wei-Jia Huang, Min-Hsiu Hsieh

Abstract: Quantum-Train is an innovative approach that leverages quantum machine learning (QML) to train classical neural network (NN) models, offering significant parameter reduction on a polylogarithmic scale, eliminating data encoding issues, and enabling inference on purely classical computers. We introduce the Quantum-Train Toolkit, which consolidates the source code from various studies on the Quantum-Train framework. This includes image classification on MNIST, FashionMNIST, and CIFAR-10 datasets using convolutional neural networks, reinforcement learning with policy networks in Cartpole-v1 and MiniGrid environments, and Long Short-Term Memory models for flood prediction. The Quantum-Train Toolkit simplifies the execution of PyTorch multilayer perceptron models using Quantum-Train and provides guidance for adapting more complex architectures into the Quantum-Train format.