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.

## Posts tagged: Machine learning

## Hot authors and topics in quant-ph in 2016 and how to generate a sexy abstract

## 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.

## Fast self-organizing maps in Python with Somoclu

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

## 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.

## Reflective random indexing on short documents with fixed vocabulary

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.

## 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.

## Training emergent self-organizing maps on sparse data with Somoclu

Self-organizing maps are computationally expensive to train -- emergent maps are even more so. This post looks at the constraints with sparse data.

## 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.

## Merging a distributional and a semantic vector space in complex Hilbert space

Describing how to build a complex-valued random index using a term and a concept vector space.