Quantum Active Learning

• URL: http://arxiv.org/abs/2405.18230v1
• Date: Tue, 28 May 2024 14:39:54 GMT
• Title: Quantum Active Learning
• Authors: Yongcheng Ding, Yue Ban, Mikel Sanz, José D. Martín-Guerrero, Xi Chen,
• Abstract summary: Training a quantum neural network typically demands a substantial labeled training set for supervised learning. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets. We elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments.
• Score: 3.3202982522589934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled training set for supervised learning. Human annotators, often experts, provide labels for samples through additional experiments, adding to the training cost. To mitigate this expense, there is a quest for methods that maintain model performance over fully labeled datasets while requiring fewer labeled samples in practice, thereby extending few-shot learning to the quantum realm. Quantum active learning estimates the uncertainty of quantum data to select the most informative samples from a pool for labeling. Consequently, a QML model is supposed to accumulate maximal knowledge as the training set comprises labeled samples selected via sampling strategies. Notably, the QML models trained within the QAL framework are not restricted to specific types, enabling performance enhancement from the model architecture's perspective towards few-shot learning. Recognizing symmetry as a fundamental concept in physics ubiquitous across various domains, we leverage the symmetry inherent in quantum states induced by the embedding of classical data for model design. We employ an equivariant QNN capable of generalizing from fewer data with geometric priors. We benchmark the performance of QAL on two classification problems, observing both positive and negative results. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets by labeling less than 7\% of the samples in the pool with unbiased sampling behavior. Furthermore, we elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments. (character count limit, see the main text)

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