Few measurement shots challenge generalization in learning to classify entanglement
This work highlights a critical problem for quantum machine learning practitioners, revealing that naive classical methods fail in quantum settings, which is incremental as it builds on existing hybrid techniques but identifies a specific bottleneck.
The paper tackles the challenge of generalization in quantum learning when limited measurement shots are available, showing that this uncertainty becomes the dominant error source in classifying maximally entangled vs. separable states, and introduces a classical shadows estimator that improves performance in big data, few copy regimes.
The ability to extract general laws from a few known examples depends on the complexity of the problem and on the amount of training data. In the quantum setting, the learner's generalization performance is further challenged by the destructive nature of quantum measurements that, together with the no-cloning theorem, limits the amount of information that can be extracted from each training sample. In this paper we focus on hybrid quantum learning techniques where classical machine-learning methods are paired with quantum algorithms and show that, in some settings, the uncertainty coming from a few measurement shots can be the dominant source of errors. We identify an instance of this possibly general issue by focusing on the classification of maximally entangled vs. separable states, showing that this toy problem becomes challenging for learners unaware of entanglement theory. Finally, we introduce an estimator based on classical shadows that performs better in the big data, few copy regime. Our results show that the naive application of classical machine-learning methods to the quantum setting is problematic, and that a better theoretical foundation of quantum learning is required.