Boosting on the shoulders of giants in quantum device calibration
This addresses the challenge of quantum device calibration for researchers and engineers, offering a significant performance improvement, though it is incremental as it builds on existing scientific models.
The paper tackles the problem of limited data in scientific domains by introducing a machine learning approach that leverages prior scientific models to improve generalizability, specifically applied to predicting the energy spectrum of a Hamiltonian on a superconducting quantum device for calibration, achieving over 20% higher accuracy than the state-of-the-art.
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."