The QLattice is a radical new machine learning technology
Born from the desire to challenge contemporary, black-box AI, the QLattice introduces a new standard of explainable AI.
What is a QLattice?
Physics is so damn “Feyn”
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
Less data needed
Scientific Discovery
The QLattice is particularly useful for AI powered scientific discovery. Since the models are simple explanations, they can serve as a scientific hypothesis to be further examined.
QLattice – Free For Science
The QLattice is free for non-commercial use. Just pip install, and get started with explainable AI through one of our tutorials
QLattice – Commercial
The QLattice is also available for commercial use and in more powerful configurations. Contact us for licensing information
Scientific Service
Our in-house scientists and bioinformaticians can help you get more our of your data, and give you the explainable edge
QLattice Publications
An Approach to Symbolic Regression Using Feyn
Kevin René Broløs, Meera Vieira Machado, Chris Cave, Jaan Kasak, Valdemar Stentoft-Hansen, Victor Galindo Batanero, Tom Jelen, Casper Wilstrup
Symbolic regression outperforms other models for small data sets
Casper Wilstrup, Jaan Kasak
Understanding the data we create
We have to understand what the decisions we make are based on, not blindly trust that the computer is right.
How I Learned to Stop Encoding and Love the QLattice
Curing your data preprocessing blues: Automatic handling of categorical data and scaling
A new kind of AI
Do you think about machine learning? How about all the research put into self-driving cars or image recognition or natural language processing?