No Free Lunch for Quantum Machine Learning

• URL: http://arxiv.org/abs/2003.14103v1
• Date: Tue, 31 Mar 2020 11:19:41 GMT
• Title: No Free Lunch for Quantum Machine Learning
• Authors: Kyle Poland, Kerstin Beer, Tobias J. Osborne
• Abstract summary: We find a lower bound on the quantum risk of a quantum learning algorithm trained via pairs of input and output states when averaged over training pairs and unitaries.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ultimate limits for the quantum machine learning of quantum data are investigated by obtaining a generalisation of the celebrated No Free Lunch (NFL) theorem. We find a lower bound on the quantum risk (the probability that a trained hypothesis is incorrect when presented with a random input) of a quantum learning algorithm trained via pairs of input and output states when averaged over training pairs and unitaries. The bound is illustrated using a recently introduced QNN architecture.

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