Title: Quantum Autoencoder: An Efficient Representation Learner for Quantum Features
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: quantum computing, quantum machine learning, quantum autoencoders, quantum representation learning, quantum unsupervised learning, Quantum Bioinformatics

Abstract: Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of quantum autoencoders in learning data-driven quantum representations. We first theoretically demonstrate that the quantum autoencoder method is efficient in terms of sample complexity throughout the entire training process. Then we numerically train the quantum autoencoder on 3 million peptide sequences, and evaluate their effectiveness across multiple peptide classification problems, including antihypertensive activity, blood–brain barrier-penetration, and cytotoxicity. The learned representations were compared against Hamiltonian-evolved baselines using a quantum kernel with support vector machines. Results show that quantum autoencoder learned representations achieve accuracy improvements ranging from 0.4\% to 8.1\% over Hamiltonian baselines across seven datasets, demonstrating effective generalization across biologically distinct datasets, with pre-training enabling effective transfer learning without task-specific fine-tuning. This work establishes that quantum autoencoder architectures can effectively learn from large-scale datasets (3 million samples) with compact parameterizations ($\sim$900 parameters), demonstrating their viability for practical quantum applications.

TL;DR: Quantum autoencders trained on 3 million peptides learn efficient representations that improve classification accuracy over baselines, showing scalable quantum pre-training for bioinformatics.