Resource-efficient quantum contrastive learning

Efficient similarity estimation between quantum states remains a key bottleneck in quantum machine learning. While quantum fidelity provides a natural similarity measure, its evaluation typically relies on pairwise Swap Test circuits, leading to quadratic scaling with batch size. We propose Resource-efficient Quantum Contrastive Learning (ReQCL), a quantum-native framework that leverages coherent superposition to estimate global similarity. By preparing a batch-level superposition and extracting a batch-averaged fidelity from a single measurement, ReQCL avoids explicit pairwise evaluation and reduces the complexity from quadratic to linear in the batch size. We further introduce a fidelity-ratio objective that contrasts individual alignments against a global similarity baseline, enabling effective optimization of quantum representations. Numerical simulations suggest that the proposed approach learns meaningful embeddings in quantum state space. Our results show that global fidelity estimation via quantum superposition provides a scalable pathway for quantum representation learning.