Exploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization

Abstract

Bioprocess optimization spans multiple scales, from molecular interactions to plant-wide operations, presenting challenges due to nonlinearity, high-dimensionality, and computational intractability. Traditional methods, such as Mixed-Integer Nonlinear Programming (MINLP), often fail to efficiently solve these problems. This work explores Hybrid Quantum-Classical (HQC) algorithms as a promising alternative. By integrating quantum solvers for discrete combinatorial optimization with classical solvers for continuous variable refinement, HQC approaches overcome computational bottlenecks inherent to classical techniques. The application of HQC algorithms enhances efficiency, scalability, and decision-making in bioprocess optimization, particularly in metabolic pathway selection, real-time fermentor control, and plant-wide resource allocation. This perspective outlines the theoretical foundation of HQC methods, their applications across bioprocess scales, and future research directions.