Boosting on the shoulders of giants in quantum device calibration

Understand

Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task.

• In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset.
• Yet in some scientific disciplines, obtaining an abundance of data is an impractical luxury, however; there is an explicit model of the domain based upon previous scientific discoveries.
• Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model.