Classical surrogates for quantum learning models
- URL: http://arxiv.org/abs/2206.11740v1
- Date: Thu, 23 Jun 2022 14:37:02 GMT
- Title: Classical surrogates for quantum learning models
- Authors: Franz J. Schreiber, Jens Eisert and Johannes Jakob Meyer
- Abstract summary: We introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model.
We show that large classes of well-analyzed re-uploading models have a classical surrogate.
- Score: 0.7734726150561088
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The advent of noisy intermediate-scale quantum computers has put the search
for possible applications to the forefront of quantum information science. One
area where hopes for an advantage through near-term quantum computers are high
is quantum machine learning, where variational quantum learning models based on
parametrized quantum circuits are discussed. In this work, we introduce the
concept of a classical surrogate, a classical model which can be efficiently
obtained from a trained quantum learning model and reproduces its input-output
relations. As inference can be performed classically, the existence of a
classical surrogate greatly enhances the applicability of a quantum learning
strategy. However, the classical surrogate also challenges possible advantages
of quantum schemes. As it is possible to directly optimize the ansatz of the
classical surrogate, they create a natural benchmark the quantum model has to
outperform. We show that large classes of well-analyzed re-uploading models
have a classical surrogate. We conducted numerical experiments and found that
these quantum models show no advantage in performance or trainability in the
problems we analyze. This leaves only generalization capability as possible
point of quantum advantage and emphasizes the dire need for a better
understanding of inductive biases of quantum learning models.
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