Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

Posted by Ryan Babbush and Jarrod McClean, Quantum Software Engineers, Quantum AI Team“The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.”-Paul Dirac, Quantum Mechanics of Many-Electron Systems (1929)In this passage, physicist Paul Dirac laments that while quantum mechanics accurately models all of chemistry, exactly simulating the associated equations appears intractably complicated. Not until 1982 would Richard Feynman suggest that instead of surrendering to the complexity of quantum mechanics, we might harness it as a computational resource. Hence, the original motivation for quantum computing: by operating a computer according to the laws of quantum mechanics, one could efficiently unravel exact simulations of nature. Such simulations…
Original Post: Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

An Updated History of R

Here’s a refresher on the history of the R project: 1992: R development begins as a research project in Auckland, NZ by Robert Gentleman and Ross Ihaka  1993: First binary versions of R published at Statlib  1995: R first distributed as open-source software, under GPL2 license 1997: R core group formed 1997: CRAN founded (by Kurt Jornik and Fritz Leisch) 1999: The R website, r-project.org, founded 2000: R 1.0.0 released (February 29)  2001: R News founded (later to become the R Journal) 2003: R Foundation founded 2004: First UseR! conference (in Vienna) 2004: R 2.0.0 released 2009: First edition of the R Journal 2013: R 3.0.0 released 2015: R Consortium founded, with R Foundation participation 2016: New R logo adopted I’ve added some additional dates gleaned from the r-announce mailing list archives and a 1998 paper on the history of R written by co-founder…
Original Post: An Updated History of R

Saving Snow Leopards with Artificial Intelligence

The snow leopard, the large cat native to the mountain ranges of Central and South Asia, is a highly endangered species. With an estimated estimated 3900-6500 individuals left in the wild, conservation efforts led by the Snow Leopard Trust are focused on preserving this iconic animal. But the snow leopard is an elusive creature: given their range and emote habitat (including the highlands of the Himalayas), they are difficult to study. In order to gather data about the creatures, researchers have used camera traps to capture more than 1 million images.  But not all of those images are of snow leopards. It’s a time-consuming process to classify those images as being of snow leopards, their prey, some other animal or nothing at all. To make things even more difficult, snow leopards have excellent camouflage, and can be difficult to spot even by…
Original Post: Saving Snow Leopards with Artificial Intelligence

Announcing dplyrXdf 1.0

I’m delighted to announce the release of version 1.0.0 of the dplyrXdf package. dplyrXdf began as a simple (relatively speaking) backend to dplyr for Microsoft Machine Learning Server/Microsoft R Server’s Xdf file format, but has now become a broader suite of tools to ease working with Xdf files. This update to dplyrXdf brings the following new features: Support for the new tidyeval framework that powers the current release of dplyr Support for Spark and Hadoop clusters, including integration with the sparklyr package to process Hive tables in Spark Integration with dplyr to process SQL Server tables in-database Simplified handling of parallel processing for grouped data Several utility functions for Xdf and file management Workarounds for various glitches and unexpected behaviour in MRS and dplyr Spark, Hadoop and HDFS New in version 1.0.0 of dplyrXdf is support for Xdf files and datasets stored…
Original Post: Announcing dplyrXdf 1.0

Data Version Control in Analytics DevOps Paradigm

[unable to retrieve full-text content]DevOps and DVC tools can help reduce time data scientists spend on mundane data preparation and achieve their dream of focusing on cool machine learning algorithms and interesting data analysis.
Original Post: Data Version Control in Analytics DevOps Paradigm

dplyrXdf 0.10.0 beta prerelease

I’m happy to announce that version 0.10.0 beta of the dplyrXdf package is now available. You can get it from Github: install_github(“RevolutionAnalytics/dplyrXdf”, build_vignettes=FALSE) This is a major update to dplyrXdf that adds the following features: Support for the tidyeval framework that powers the latest version of dplyr Works with Spark and Hadoop clusters and files in HDFS Several utility functions to ease working with files and datasets Many bugfixes and workarounds for issues with the underlying RevoScaleR functions This (pre-)release of dplyrXdf requires Microsoft R Server or Client version 8.0 or higher, and dplyr 0.7 or higher. If you’re using R Server, dplyr 0.7 won’t be in the MRAN snapshot that is your default repo, but you can get it from CRAN: install.packages(“dplyr”, repos=”https://cloud.r-project.org”) The tidyeval framework This completely changes the way in which dplyr handles standard evaluation. Previously, if…
Original Post: dplyrXdf 0.10.0 beta prerelease