CV systems are hard to get started with, but the package Strawberry Fields provides a nice framework and a perhaps even nicer documentation for beginners. Here we study a simple variant of the CHSH inequality in the CV setting.

The intuition behind the process matrix formalism in quantum physics from the perspective of probability theory

Semidefinite programming relaxations of polynomial optimization problems of commuting variables with moment constraints, parametric and bilevel variants.

Somoclu, a massively parallel implementation of self-organizing maps, has updated its visual capabilities for its Python interface.

A brief overview of SDP solvers and tools in Python

Optimal randomness generation from entangled quantum states: computational appendix to arXiv:1505.03837.

It is possible to detect a rank loop in the hierarchy of SDP relaxations of polynomial optimization problems, but an arbitrary-precision SDP solver is recommended.

In pursuit of open science, academics should blog. As good nerds, they should opt for static website generators. Pelican is a great option, but caveats apply.

Load Cython modules from Pypy while also using Numpy.

Using SymPy, it is easy to calculate the Jordan-Wigner transformation in Python.

IPython's notebook interface combined with git and GitHub makes a perfect tool to promote reproducible academic publications.

Calculating the ground state of spinless fermions on an open two-dimensional lattice with density matrix renormalization group using ALPS.

Smartphones are powerful enough to run a Linux with a graphical interface: computer algebra systems are the next frontier in mobile computing.

A combination of Spyder, IPython, SymPy, NumPy, and Matplotlib gets pretty close to replace Mathematica in most of my use cases.

Second-order semidefinite relaxation of a constrained commutative polynomial optimization problem using PICOS in Python, exporting to SDPA.

A Python solution to generate monomials of noncommutative variables.