Python tools for analyzing both classical and quantum Bayesian Networks
Jupyter Notebook C++ Python HTML C Objective-C
Latest commit 5acd306 Feb 23, 2017 @rrtucci rrtucci improved path manipulations in bnlearn-qfog-comparison notebooks. Now…
… you don't have to insert quantum fog absolute path by hand

Quantum Fog at GitHub

What is Quantum Fog?

Quantum Fog (QFog) is an app for modelling physical situations that exhibit quantum mechanical behavior. It's a tool for investigating and discussing quantum measurement problems graphically, in terms of network diagrams called quantum Bayesian networks.

Quantum Bayesian Networks (QB nets) are a quantum mechanical version of the classical Bayesian networks (CB nets) which earned Judea Pearl a Turing Prize.

QFog is loosely based on an older app written in C++ for the Mac. Our near term plans are to write a new app, mostly written in Python, in the cloud and taking advantage of Apache-Spark technology, that integrates seamlessly CB nets and QB nets.

Ultimately, we would like to use a QFog based app to program quantum computers in a graphical, QB net based way.

Qubiter at GitHub (see is a twin project started by the same people. We hope that eventually Quantum Fog will call Qubiter to perform some tasks, like quantum compiling.

We believe QFog will also prove very useful to

  • teachers of quantum mechanics, at all levels starting from high school.
  • researchers in fields other than quantum computing (for example, quantum artificial intelligence, quantum chemistry and quantum cognition).

Project Information

  • QFog is licensed under the BSD license (3 clause version) with an added clause at the end, taken almost verbatim from the Apache 2.0 license, granting additional Patent rights. See

  • QFog at GitHub is based on older, formerly proprietary software with the same name for the Mac. Read for details about legacy history.


(Alphabetical Order)

  • Dekant, Henning
  • Tregillus, Henry
  • Tucci, Robert
  • Yin, Tao