Enhanced Reinforcement Learning-driven Process Design via Quantum Machine Learning

In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning (QML) is proposed. QML leverages theoretical advantages over classical methods to accelerate searches in large spaces. Built upon our prior work, the approach begins with a maximum set of available unit operations, represented in a flowsheet structure using an input-output stream matrix as RL observations. A Deep Q-Network (DQN) algorithm trains a parameterized quantum circuit (PQC) in place of a classical neural network (NN). The design structures generated by the RL agent are optimized using the IDAES Process Systems Engineering Framework, with the optimization objectives used as rewards to RL (e.g., cost, productivity). This quantum-enhanced algorithm is performed on a hydrodealkylation process case study, showcasing its efficiency and improved performance in navigating complex design spaces.