The intuition behind the process matrix formalism in quantum physics from the perspective of probability theory
Posts tagged: Quantum information theory
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
A quick comparison of Trotter-Suzuki-MPI, GPELab, and GPUE for simulating the evolution of Bose-Einstein Condensates
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: computational appendix to arXiv:1505.03837.
Looking at the crop of quantum machine learning manuscripts on arXiv from the beginning of 2015 until the middle of May.
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.
Another handful of papers on quantum machine learning that appeared in the last two months of 2014, and perhaps slightly earlier.
A quick overview of a handful of papers on quantum machine learning that appeared recently.
New characterizations of Bell inequalities in terms of causal structures are emerging: they can give rise to quantum versions of Bayesian networks.
Classical regression, induction, transduction and the quantum learning of unitaries, plus making the difference explicit to process tomography.
Using SymPy, it is easy to calculate the Jordan-Wigner transformation in Python.
The optimal estimation of a group of unitary transforms allows for learning an unknown function: this is similar to regression in classical machine learning.
Training least squares support vector machines on quantum hardware results in exponential speedup; we take a machine learning perspective at the new algorithm.