Single-shot quantum machine learning

Abstract

Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because the outputs of quantum learning models are inherently random. This creates an often considerable overhead, as many executions of a quantum learning model have to be aggregated to obtain an actual prediction. In this work, we analyze when quantum learning models can evade this issue and produce predictions in a near-deterministic way -- paving the way to single-shot quantum machine learning. We give a rigorous definition of single-shotness in quantum classifiers and show that the degree to which a quantum learning model is near-deterministic is constrained by the distinguishability of the embedded quantum states used in the model. Opening the black box of the embedding, we show that if the embedding is realized by quantum circuits, a certain depth is necessary for single-shotness to be even possible. We conclude by showing that quantum learning models cannot be single-shot in a generic way and trainable at the same time.

arXiv comment12+6 pages

References in corpus (12)

• Barren plateaus in quantum neural network training landscapes
• A rigorous and robust quantum speed-up in supervised machine learning
• Generalization in quantum machine learning from few training data
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• On the Quantum versus Classical Learnability of Discrete Distributions
• Understanding quantum machine learning also requires rethinking generalization
• Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing
• Upper bounds on the error probabilities and asymptotic error exponents in quantum multiple state discrimination
• Reduction of finite sampling noise in quantum neural networks
• Training robust and generalizable quantum models
• A super-polynomial quantum-classical separation for density modelling
• Statistical Complexity of Quantum Learning

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