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.
You pay a much higher price than the article processing fee if you publish in Scientific Reports: your inbox and your reputation are at stake.
The intuition behind the process matrix formalism in quantum physics from the perspective of probability 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
Your high-impact and very important journal will stay on top if and only if you publish papers written exclusively by well-funded groups of famous scientists. Save yourself the trouble of having to deal with small fish by following these 7 easy steps.
SciRate gives instant gratification for our precious preprints on arXiv. We analyse the metadata of the papers that appeared in quant-ph in 2016 to find out the hottest authors and topics, and we train a recurrent neural network to generate fake abstracts.
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.
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
We waste tax payers money on an outrageously bad piece of VPN software that also opens backdoors for government and industrial espionage.
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.
Reflective random indexing can lead to strange results: if the vocabulary is small compared to the number of documents, term vectors will show little variety.
Another handful of papers on quantum machine learning that appeared in the last two months of 2014, and perhaps slightly earlier.
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.
Benchmarking the speed of random number generation in C++11 with GCC and with VSL and ICC.
Load Cython modules from Pypy while also using Numpy.
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.
Cookies, browser-fingerprinting, tracking, and blocking of Tor exit nodes are becoming standard strategies while reading abstracts and exporting citations.
GCC, ICC, PGI compilers with BLAS/LAPACK, MKL, and ACML are compared in solving an SDP with SDPA.
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.
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.
Why would you use the spyware operating system installed on an iMac when you can run Arch Linux from an SD card?
ACM's relationship with NSA is far tighter than one could wish.
Self-organizing maps are computationally expensive to train -- emergent maps are even more so. This post looks at the constraints with sparse data.
Converting LaTeX to word processor formats is the holy grail of document conversion. Perhaps we are getting closer to a viable solution.
The optimal estimation of a group of unitary transforms allows for learning an unknown function: this is similar to regression in classical machine learning.
Smartphones are powerful enough to run a Linux with a graphical interface: computer algebra systems are the next frontier in mobile computing.
You are legally allowed to put a PDF of your paper online. Pre-publication manuscripts can be published in an institutional repository or on arXiv.
Unsolicited calls for papers flood our inboxes: here we analyse three months of academic spam to identify the sources.
Training least squares support vector machines on quantum hardware results in exponential speedup; we take a machine learning perspective at the new algorithm.
ACM member's forwarding email address must undergo filtering by Google. Your email correspondence cannot escape the attention of NSA.
A combination of Spyder, IPython, SymPy, NumPy, and Matplotlib gets pretty close to replace Mathematica in most of my use cases.
Describing how to build a complex-valued random index using a term and a concept vector space.
Getting around Fortran-style array indexing in CuBlas from C code without transponation. Bonus Thrust vector casting added.
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.
Thrust-based summing of the elements of a submatrix at a given offset according to a stencil.
A detailed description of how to use Thrust reduce by key to calculate the argmins of the rows of a matrix