# \[ANN\] BayesianTomography.jl - A Julia package for Bayesian quantum state tomography **URL:** https://discourse.julialang.org/t/ann-bayesiantomography-jl-a-julia-package-for-bayesian-quantum-state-tomography/112764 **Category:** Package Announcements **Created:** [April 10, 2024, 11:31am UTC](https://discourse.julialang.org/t/ann-bayesiantomography-jl-a-julia-package-for-bayesian-quantum-state-tomography/112764 "2024-04-10T11:31:31Z") **Posts on this page:** 1 **Page:** 1

### Author: ![marcsgil](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marcsgil/32/33908_2.png) [@marcsgil](https://discourse.julialang.org/u/marcsgil) #### Post date: [April 10, 2024, 11:31am UTC](https://discourse.julialang.org/t/ann-bayesiantomography-jl-a-julia-package-for-bayesian-quantum-state-tomography/112764/1 "2024-04-10T11:31:31Z")

Hello!! I’m pleased to announce the package [BayesianTomography.jl](https://github.com/marcsgil/BayesianTomography.jl). As the name suggests, its aim is to perform Quantum State Tomography using Bayesian inference. The basic ideia is that the outcomes of a quantum experiment define a posterior distribution in the space of quantum states. Then, in a certain sense, the best possible point prediction for the state that produced our results is the mean of this posterior. We utilize a Metropolis Adjusted Langevin Algorithm (MALA) to perform the sampling. I have also written a [documentation](https://marcsgil.github.io/BayesianTomography.jl/dev/) explaining the usage of the package. The basic usage can be seen [here](https://marcsgil.github.io/BayesianTomography.jl/dev/usage/). There is also a section explaining the [theory](https://marcsgil.github.io/BayesianTomography.jl/dev/theory/) (there is a nice animation there, go check it out 😉). We also expose the [docstrings of every defined function](https://marcsgil.github.io/BayesianTomography.jl/dev/api/). I hope you find this package useful. Any doubts/suggestions/contributions are extremely welcome!