QSEA: Quantum Self-supervised Learning with Entanglement Augmentation

TL;DR

QSEA introduces a quantum self-supervised learning method that leverages entanglement and fidelity-based loss to enhance feature representation, outperforming existing quantum SSL methods and demonstrating robustness across benchmarks.

Contribution

It proposes a novel entanglement-based sample augmentation and fidelity-driven loss function for quantum SSL, advancing quantum machine learning capabilities.

Findings

QSEA outperforms existing quantum SSL methods on multiple benchmarks.

QSEA exhibits stronger stability in noisy environments.

The framework provides a foundation for future quantum learning systems.

Abstract

As an unsupervised feature representation paradigm, Self-Supervised Learning (SSL) uses the intrinsic structure of data to extract meaningful features without relying on manual annotation. Despite the success of SSL, there are still problems, such as limited model capacity or insufficient representation ability. Quantum SSL has become a promising alternative because it can exploit quantum states to enhance expression ability and learning efficiency. This letter proposes a Quantum SSL with entanglement augmentation method (QSEA). Different from existing Quantum SSLs, QSEA introduces an entanglement-based sample generation scheme and a fidelity-driven quantum loss function. Specifically, QSEA constructs augmented samples by entangling an auxiliary qubit with the raw state and applying parameterized unitary transformations. The loss function is defined using quantum fidelity, quantifying similarity between quantum representations and effectively capturing sample relations. Experimental results show that QSEA outperforms existing quantum self-supervised methods on multiple benchmarks and shows stronger stability in decorrelation noise environments. This framework lays the theoretical and practical foundation for quantum learning systems and advances the development of quantum machine learning in SSL.