Quantum Machine Learning via Contrastive Training
This work addresses the problem of data efficiency in quantum machine learning for researchers and practitioners, offering an incremental improvement over existing methods by integrating contrastive learning into quantum hardware.
The paper tackles the challenge of labeled data scarcity in quantum machine learning by introducing self-supervised pretraining of quantum representations using contrastive training on a trapped-ion quantum computer, resulting in higher mean test accuracy and lower variability in image classification, especially with limited labeled data.
Quantum machine learning (QML) has attracted growing interest with the rapid parallel advances in large-scale classical machine learning and quantum technologies. Similar to classical machine learning, QML models also face challenges arising from the scarcity of labeled data, particularly as their scale and complexity increase. Here, we introduce self-supervised pretraining of quantum representations that reduces reliance on labeled data by learning invariances from unlabeled examples. We implement this paradigm on a programmable trapped-ion quantum computer, encoding images as quantum states. In situ contrastive pretraining on hardware yields a representation that, when fine-tuned, classifies image families with higher mean test accuracy and lower run-to-run variability than models trained from random initialization. Performance improvement is especially significant in regimes with limited labeled training data. We show that the learned invariances generalize beyond the pretraining image samples. Unlike prior work, our pipeline derives similarity from measured quantum overlaps and executes all training and classification stages on hardware. These results establish a label-efficient route to quantum representation learning, with direct relevance to quantum-native datasets and a clear path to larger classical inputs.