Matchless performance in Exploration, Segmentation & Prediction

Quantum Insights interprets data even when you don’t know what the right answer looks like.  Our tool leverages techniques from theoretical physics and quantum mechanics into a hypothesis-free approach that shows you the hidden clusters and structures in your data.  Most importantly, we can address data that is too complex or high-dimensional for most techniques.  The underlying Dynamic Quantum Clustering algorithm is like nothing else.

No Hypotheses

No Cleaning

No Operator Bias

No Dimensional Reduction

Quantum Insights identifies 48 of 73,000 mRNA expressions as predictors of cancer. 

Our technique also identified patient age, cancer stage, and a relationship between glioblastoma and low-grade glioma, entirely from genotypic data.  Read about it in Nature, Scientific Reports.



The emergence of big data, as well as advancements in data science approaches and technology, is providing pharmaceutical companies with an opportunity to gain novel insights that can enhance and accelerate drug development.


Genomic medicine is attempting to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics can uncover hidden patterns, unknown correlations, and other insights by examining a variety of large-scale data sets.


With no physical products to manufacture, data (the source of information) is one of financial services companies’ most important assets.  Finding hidden insights in the data can deliver invaluable competitive differentiation.


As sensors spread across almost every industry, the internet of things is triggering a massive influx of big data.

One of the big unsolved problems of the modern world is to convert information into understanding. This is a big problem in science, in government, in business administration and in economics. In each of these areas we have huge amounts of information and very limited understanding. As a result of the rapid progress of digital technology, it has become far cheaper to collect information than to understand it. Recently a new method called Dynamic Quantum Clustering has been invented to extract small nuggets of understanding from large amounts of information. I am grateful to my friend Marvin Weinstein, who is one of the inventors of DQC, for telling me about it.

Analysis of big data is all the rage today. The object is to find needles in haystacks. If you know what your particular needle looks like, then there are lots of programs you can use to look. But, what if you don’t know if you are looking for a needle, or a diamond, or a horseshoe, of a nest of baby mice?

Then you call on DQC, which – as far as I know – is the only search algorithm that doesn’t care what is there. It will find anything that is different from the hay no matter what its shape. It is also different in that it is not sensitive to how complex the data is. Adding more features only causes a linear increase in the time it takes for the algorithm to do its work. Existing systems tend to work with at most a few features at a time.

DQC is something genuinely new and its limitations are yet to be determined. Whatever these limitations may be, they are certainly fewer than existing search programs.

I have been working in the area of statistical learning algorithms and large scale scientific computing for many years. Dynamic Quantum Clustering (DQC) is the most exciting new way of looking at data that I have seen in a long time. It provides a powerful, entirely new way to attack problems in translational medicine that cannot be touched my more conventional methods.

I have worked in the areas of extreme-scale data analytics, high-assurance cyber security, graph-theoretic knowledge systems, and high-performance computation for many years. Dynamic Quantum Clustering is an intriguing new way of looking at big, complex, high-dimensional datasets. It frees the investigator from the straight-jacket of query or hypothesis based analysis and lets the data speak for itself. I want to thank Marvin Weinstein for bringing this exciting new approach to my attention.

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