Exploring Chemical Space with Chemistry-Inspired Dynamic Quantum Circuits in the NISQ Era.

Abstract

The generation of chemical molecular structures is crucial for advancements in drug design, materials science, and related fields. With the rise of artificial intelligence, numerous generative models have been developed to propose promising molecular structures to specific challenges. However, exploring the vast chemical space using classical generative models demands extensive chemical structure data, considerable computational resources, and a large number of model parameters, which hinders their efficiency. Quantum computing presents a promising alternative by exploiting quantum parallelism and entanglement, potentially reducing the computational overhead required for such tasks. In this study, we designed a quantum-based molecular generator (QMG) specifically tailored to generate small molecules containing carbon, nitrogen, and oxygen atoms. This model imposes strict constraints on the quantum circuit's output quantum states, significantly eliminating mathematically invalid connection graphs and enabling more efficient sampling of valid molecular structures. Remarkably, our quantum circuit, utilizing only 134 parameters, is capable of enumerating all molecular structures comprising up to nine heavy atoms, showcasing the parameter efficiency achievable through quantum superposition and entanglement. Our experimental results show that the output generated by this circuit exhibits a high degree of validity and uniqueness after Bayesian optimization, showing comparable performance to classical deep generative models. Furthermore, by fixing specific parameters in the quantum circuits, the quantum generator can constrain the chemical space and exclusively generate chemical molecules containing specified functional groups. This feature underscores its potential value for targeted applications in specific domains, especially in drug discovery. Overall, this compact design not only reduces parameter demands but also enables efficient exploration of a nontrivial portion of chemical space, demonstrating a key advantage of quantum-based generative models over larger classical counterparts.