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Table of Contents
Abstract
1 Introduction
2 Classical Invertible Neural Networks
2.1 Density Estimation using Invertible Models
2.2 Maximum Mean Discrepancy
3 Invertible Quantum Neural Networks
3.1 Inverse State Preparation
Summary
The document discusses Generative Invertible Quantum Neural Networks and their application in quantum computing. It introduces Quantum Neural Networks (QNNs) and their advantages over classical neural networks. The document explores the concept of Quantum Invertible Neural Networks (QINN) and their potential in density estimation and generative tasks. It compares classical Invertible Neural Networks with Quantum Invertible Neural Networks, highlighting the benefits of quantum computation. The text also delves into the technical aspects of quantum circuits and state preparation in the context of QNNs. Overall, the document provides insights into the intersection of quantum computing and machine learning.