What is Qiboml?

Qiboml is an open-source and full-stack quantum machine learning package based on Qibo. Among the goals of Qiboml we have:

• provide automatically differentiable backends for any Qibo application;

• render Qibo circuits as differentiable layers of well-known machine learning frameworks such as Tensorflow and Pytorch;

• extend quantum machine learning algorithm execution to self-hosted devices through Qibolab;

• integrate hardware-level routines into higher-level quantum machine learning algorithms through Qibocal.

How to Use the Documentation

Welcome to the comprehensive documentation for Qiboml! This guide will help you navigate through the various sections and make the most out of the resources available.

Contents

Introduction

• Getting started
  • Installation instructions
  • Building your first Quantum Model
  • Switching the ML Interface
  • Switching the Execution Backend
  • Switching the Autodifferentiation engine
• Advanced use cases
  • Defining a custom Encoder
  • Defining a custom Decoder

Tutorials

• Binary Classification on the MNIST dataset
  • Installing Requirements
  • Dataset Preparation
  • Construct the Image Encoder
  • Define the Quantum model
  • Set up a custom activation for the Quantum model
  • Put everything together
  • Baseline performance evaluation
  • Train the model
  • Evaluate the trained model

Main documentation

• API Reference
• Developer guides

Documentation links

• Qibo docs
• Qibolab docs
• Qibocal docs
• Qibosoq docs
• Qibochem docs
• Qiboml docs
• Qibotn docs
• Qibo-cloud-backends docs

Indices and tables

• Index

• Module Index

• Search Page