Triplet Loss Based Quantum Encoding for Class Separability
This work addresses the challenge of efficient quantum encoding for complex datasets like images in quantum machine learning, representing an incremental improvement over existing methods.
The paper tackled the problem of improving variational quantum classifiers by proposing a data-driven quantum encoding scheme that enhances class separability, resulting in considerable performance gains on MNIST and MedMNIST datasets with lower circuit depth compared to amplitude encoding.
An efficient and data-driven encoding scheme is proposed to enhance the performance of variational quantum classifiers. This encoding is specially designed for complex datasets like images and seeks to help the classification task by producing input states that form well-separated clusters in the Hilbert space according to their classification labels. The encoding circuit is trained using a triplet loss function inspired by classical facial recognition algorithms, and class separability is measured via average trace distances between the encoded density matrices. Benchmark tests performed on various binary classification tasks on MNIST and MedMNIST datasets demonstrate considerable improvement over amplitude encoding with the same VQC structure while requiring a much lower circuit depth.