Why zyglrox?

At the moment, other quantum computation python frameworks lack the performance needed for quantum machine learning applications. By building a simulator on top of Google’s TensorFlow, we can easily utilize the GPU to speed up computations where necessary. Additionally, zyglrox can easily be plugged into a wide range of deep learning models, enabling fast experimentation with hybrid quantum machine learning models.

DISCLAIMER: This package is not a Google product and is in no way associated with Tensorflow Quantum, Google’s official Tensorflow Quantum Simulator. Additionally, zyglrox is based on TensorFlow 1.x, whereas Tensorflow Quantum is based on TensorFlow 2.x.

No I mean why did you name it zyglrox?

Well, because of reasons and because it contains x, y and z so it sounds techy.

Getting Started

• Installation

zyglrox

• Core
  • Circuit
  • Gates
  • Observables
  • Hamiltonians
  • Circuit Templates
  • Topologies
  • Edge Coloring
  • Optimizers
  • Utils
• Models
  • Quantum Boltzmann Machine
  • Quantum Variational Eigensolver

Tutorials

• Beginner
  • 1. Getting Started
  • 2. Multi-Qubit gates and Visualization
• Intermediate
  • 1. Making Things
  • 2. Doing Stuff
• Advanced
  • 1. Learning Magic
  • 2. Practicing Magic

Indices and tables

• Index

• Module Index

• Search Page