QOBRA: A Quantum Operator-Based Autoencoder for De Novo Molecular Design

Abstract

We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA le circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoderand decoder unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase QOBRA as applied to de novo design of Ca2+-, Mg2+-, and Zn2+-binding metalloproteins after training the generative model with a modest dataset. Significance StatementRecent advancements in classical generative machine learning have shown significant strides in molecular design for targeted applications. Nonetheless, these advancements are fundamentally limited by classical computation based on binary units. We introduce a quantum computation-based ML framework employing qubits, which exhibits the ability to synthesize de novo molecular instances with specified properties from limited datasets. Quantum networks require exponentially fewer parameters than classical ones, enhancing their trainability and efficiency. While our demonstration focuses on metalloprotein primary sequences, the paradigm is adaptable to diverse molecular designs. This integration of AI and quantum computing holds potential to expand the scientific and technological frontiers of both domains within a practical framework.