Recently discovered a fascinating book, Geometry of Quantum States, An Introduction to QUANTUM ENTANGLEMENT. While not exclusively about quantum computing it does give a clear exposition of quantum mechanical concepts from a geometric perspective, including qubits, POVM, and quantum information theory. Written by I. Bengtsson and K. Zyczkowski (Cambridge University Press).

See the latest developments from D-wave, Google, Microsoft and IBM Q.

D-wave has successfully applied quantum computing in conjunction with Volkswagen to traffic optimisation!

The IBM Q experience provides clear explanations of many aspects of quantum computing, as well as an opportunity to run code on an actual real quantum computer. More importantly, it also supports an active quantum computing community.

Quantum cloud services are available from Rigetti.

Just to be clear, I have no affiliation with any of these companies.

Quantum computing control systems are now commercially available! Details not here because we are not affiliated, but was a pleasant surprise in the latest *Australian Physics*.

**Biochemical Property Prediction: **

BioPPSy is a package to predict clinically relevant properties of small molecules from those molecules from which such properties are already known. It works by training the chosen model with a given set of molecules and then using the trained model to predict the desired property, typically membrane permeability, to molecules for which this is unknown. When I took over this project it only had linear models available and was not yet on a public repository. Apart from some debugging and improving its structure in places, I have since added Partial Linear Squares, Neural Networksand Support Vector Regression (these last two by implementing the weka package). Just as importantly, this has been done in such a way that anyone can code their own algorithm and simply add it to the project. BioPPsy may be downloaded from my sourceforge repository.

**Bayesian networks of data from a cohort study:**

Stand by for inferred Bayesian networks concerning the relationship between early respiratory infections and childhood asthma pathogenesis.

**Biological subtypes of asthma from exclusive predictors:**

A variable predictive of only a given subtype will be weakly predictive of the more general case. For example, the level of certain antibodies in the blood can predict some cases of allergic asthma but says nothing about the likelihood of developing asthma that is not allergy related, so the predictive power for asthma in general falls in-between. A careful analysis found that the corresponding Areas Under the Curve are related in a way determined by the fraction of cases belonging to the predictable subtype. Should this relationship not hold then the predictive variable in question is not exclusive to the subtype chosen, *i.e.* it is predictive of subtypes other than the one tested. More to come...