Quantum computing represents a revolutionary approach to computation that harnesses the principles of quantum mechanics to perform tasks that are intractable for classical computers. At its core, quantum computing leverages quantum bits, or qubits, which can exist in superposition states, enabling them to represent multiple values simultaneously. Additionally, qubits can be entangled, allowing correlations between them that can be exploited for computation.

One of the most promising applications of quantum computing lies in its potential to solve complex optimization problems much more efficiently than classical computers. This includes tasks such as factoring large numbers, optimizing supply chains, and simulating molecular structures for drug discovery.

Currently, quantum computing is still in its infancy, with practical quantum computers consisting of only a few qubits. However, significant progress is being made by research institutions, tech companies, and start-ups worldwide to develop scalable quantum hardware and algorithms. While practical quantum computers capable of outperforming classical computers in specific tasks are still on the horizon, the field is advancing rapidly, and quantum computing is poised to revolutionize various industries in the coming years.

In our research, we investigate the advancements in Process System Engineering (PSE) by integrating computational methodologies and tools to incorporate next-generation technologies such as Support Vector Machine (SVM) metamodels and Quantum Computing into PSE workflows. We use Python programming language to create an interface that interconnects Aspen Plus and Activity Browser, a graphical user interface for the Brightway2 LCA framework, to accelerate process modelling, simulation, and Life Cycle Assessment (LCA) while bridging the gap between process simulation and environmental impact assessment.

We conduct multiple sensitivity analyses and use the automated interface framework to generate preliminary ReCiPE indicators for LCA. Additionally, we compare the performance of classical Support Vector Regression (SVR) models versus quantum SVR models. We transform classical machine learning models into quantum models using parametrized quantum circuits in Python's scikit-learn and Qiskit packages.

Our preliminary results demonstrate the quantum SVR capabilities to reinforce more efficient, accurate, and sustainable automated process simulation optimization for next-generation process design and assessment approaches.