MolQAE: Quantum Autoencoder for Molecular Representation Learning

We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps Simplified Molecular Input Line Entry System (SMILES) strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecular characteristics and a remarkable preservation of fidelity, approaching robust molecular reconstruction even with substantial dimensionality reduction. Our model introduces a novel quantum pathway in cheminformatics, capable of processing complete molecular structural information with a dedicated quantum architecture considering the Noisy Intermediate-Scale Quantum (NISQ)-era development and promising significant advances in drug and materials discovery.