Boosting on the shoulders of giants in quantum device calibration

• URL: http://arxiv.org/abs/2005.06194v1
• Date: Wed, 13 May 2020 07:59:57 GMT
• Title: Boosting on the shoulders of giants in quantum device calibration
• Authors: Alex Wozniakowski, Jayne Thompson, Mile Gu, Felix Binder
• Abstract summary: We introduce a new approach to machine learning that is able to leverage prior scientific discoveries. We show its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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."

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