The intuition behind the process matrix formalism in quantum physics from the perspective of probability theory
Posts tagged: Python
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.