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arXiv · 2003.14103

No Free Lunch for Quantum Machine Learning

Kyle Poland·Kerstin Beer·Tobias J. Osborne
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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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Kyle Poland, Kerstin Beer, Tobias J. Osborne. 2020-03-31. No Free Lunch for Quantum Machine Learning. https://arxiv.org/abs/2003.14103

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