Shadows of quantum machine learning

TL;DR

This paper introduces a new class of quantum machine learning models that require quantum resources only during training, enabling classical deployment and demonstrating a provable learning advantage over classical models.

Contribution

It proposes a universal quantum training framework with classical deployment, bridging the gap between quantum training advantages and practical use.

Findings

Models are universal for classically-deployed quantum ML

They have restricted learning capacities compared to fully quantum models

They achieve a provable advantage over classical learners under certain assumptions

Abstract

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a new class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a 'shadow model' from which the classical deployment becomes possible. We prove that: i) this class of models is universal for classically-deployed quantum machine learning; ii) it does have restricted learning capacities compared to 'fully quantum' models, but nonetheless iii) it achieves a provable learning advantage over fully classical learners, contingent on widely-believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.