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

## Posts tagged: Quantum information theory

## From stochastic matrices to process matrices

## Advances in quantum machine learning in 2016 and in early 2017

Desperately trying to keep up with the latest developments in quantum machine learning, let that be a new quantum-enhanced learning protocol, or some exciting connection between quantum many-body physics and statistical learning theory

## Comparing three numerical solvers of the Gross-Pitaevskii equation

A quick comparison of Trotter-Suzuki-MPI, GPELab, and GPUE for simulating the evolution of Bose-Einstein Condensates

## Quantum machine learning in 2015

Quantum machine learning as a research field is exploding: here we give a brief overview of the relevant papers that appeared on arXiv in 2015.

## Optimal randomness generation from entangled quantum states

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

## Machine learning and quantum physics in the first third of 2015

Looking at the crop of quantum machine learning manuscripts on arXiv from the beginning of 2015 until the middle of May.

## Detecting a rank loop in the NPA hierarchy

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.

## End-of-year updates on quantum machine learning

Another handful of papers on quantum machine learning that appeared in the last two months of 2014, and perhaps slightly earlier.

## Some recent advances in quantum machine learning

A quick overview of a handful of papers on quantum machine learning that appeared recently.

## Causal structures, Bayesian nets, and quantum systems

New characterizations of Bell inequalities in terms of causal structures are emerging: they can give rise to quantum versions of Bayesian networks.

## More on the quantum learning of unitaries, process tomography, and classical regression

Classical regression, induction, transduction and the quantum learning of unitaries, plus making the difference explicit to process tomography.

## The Jordan-Wigner transformation in Python

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

## Quantum process tomography and machine learning

The optimal estimation of a group of unitary transforms allows for learning an unknown function: this is similar to regression in classical machine learning.

## Understanding quantum support vector machines

Training least squares support vector machines on quantum hardware results in exponential speedup; we take a machine learning perspective at the new algorithm.