Abstract
In this work, we present quantum reinforcement learning algorithms for process flowsheet synthesis. Particularly, we discuss the implementation of encoding strategies to improve the algorithmic scalability. Reinforcement learning (RL)-driven flowsheet synthesis techniques provide a promising approach for conceptual process design, in addition to traditional optimization-based methods. These RL-based strategies identify the optimal flowsheet configurations from a maximum set of available processing units, without requiring to pre-postulate an interconnected superstructure. However, the resulting combinatorial design space for RL can scale extensively with the increased number of available processing units, which can render the algorithms to be computationally intensive or even intractable. To address this challenge, our prior work has introduced a quantum-enhanced approach to RL-driven process synthesis. However, this algorithm was limited in its capacity to solve larger flowsheeting problems as a large number of qubits were needed. To reduce qubit requirements and improve scalability, this work presents two different encoding strategies to embed information into the gates of quantum circuits instead of embedding it into qubits alone. We demonstrate the efficacy of these algorithms on three different scenarios of a flowsheet synthesis problem and interpret the results obtained from the IonQ quantum computing platform.