The QLattice is a powerful explainable machine learning technology

Born from the desire to challenge black-box AI, the QLattice is a symbolic AI system that introduces a new standard of explainable AI.

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What is a QLattice?

The QLattice is a supervised machine learning method for symbolic regression. It is a technology invented by Abzu and inspired by Richard Feynman’s path integral formulation. That’s why we’ve named our Python library Feyn, and the Q in QLattice is for Quantum.

The QLattice is similar to machine learning methods such as Random Forest, Gradient Boosting, and Deep Learning in that it creates predictive models from data.

Explainable and Interpretable

The main advantage of the QLattice is that the models are readily interpretable and explainable. The user is not left with a black-box prediction but also with an explanation for the prediction.

The QLattice won 1st and 2nd place in the recent SRBench Competition 2022: Interpretable Symbolic Regression for Data Science

Less data needed

The QLattice delivers good models and reasonable explanations, even with very little data. This is particularly important in pharma and life sciences, where data is not usually “big”.

Scientific Discovery

The QLattice enables AI-powered scientific discovery. Since the models are simple explanations, they can serve as a scientific hypothesis to be examined further.