  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. We show its efficacy in predicting
the entire energy spectrum of a Hamiltonian on a superconducting quantum
device, a key task in present quantum computer calibration. Our accuracy
surpasses the current state-of-the-art by over $20\%.$ Our approach thus
demonstrates how artificial intelligence can be further enhanced by "standing
on the shoulders of giants."
