Quantum Data Analytics promises the most radical shift in how future quantum based computer systems will process large data set that have little in the way of intuitive isomorphic relationships. This article is a window into experimental results being produced by Toridion Projects latest quantum tunnelling technology.

Plot of US Unemployment from 1968 to 2013:

Plot of Gold prices from 1968 to 2013:

Plot of Oil prices from 1968 to 2013:

The above graphs show the relative amplitude of Gold V Unemployment and the barrel price of Oil for the range 1968 to 2013. On first inspection there appears little to connect the three sets of data together. Month for month there appears to be no “isomorphic” relationship that would indicate a rise here = a fall there. The data sets themselves contain on average 540 points covering every month for 45 years. So, can Quantum Big Data Analytics see anything interesting by just looking at these three graphs?

In recent experiments carried out on Toridion’s 129.0.1 quantum engine all three datasets were processed and remapped into a proprietary format. The output from the quantum system was then visualised as graphs. (see below)

Quantum analytics output for Unemployment 1968-2013:

Quantum analytics output for Gold prices 1968-2013:

Quantum analytics output for Oil barrel prices 1968-2013:

When viewed in this way, very strong correlations can be seen between the “quantum” signatures of Gold, Unemployment and Oil.

Quantum analytics output for Gold prices V Unemployment 1968-2013

As we learn more about mapping stochastic patterns between seemingly unrelated sets of data the power of quantum data analytics begins to become clearer. The remarkable aspect of quantum processing is the lack of a need to define to many parameters and avoid walking every single permutation or possibility.

As an update to this post I have added the graph below that shows the periodicity of strongly affecting systems that are strongly entangled with Gold and Unemployment patterns.

The graph X axis is non linear and you can see the frequency of strongly causal events marked in Years. Whilst the research is still to some degree in the early stages the above graph demonstrates how quantum algorithms are able to rapidly identify frequency patterns in what appear to be unrelated data sets and identify the signature of potentially causal factors in their evolution. The key power of quantum versus classical big data analytics is in speed and the detection of relationships that may take a regular computation approach a very long time (if even possible) to compute.

Finally a compound graph of Oil (Green) Gold (Yellow) and Unemployment (Teal) that shows the susceptibility to 11,22 & 34 Year cycles. Gold being the only one to have mid term stability in the 4.5 to 7.5 year period.

Article originally published by Scot on LinkedIn.