Exploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization

doi:10.1021/acs.iecr.4c04624

Exploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization

2025-04-21

0

PRE

AI

I

Ilse María Hernández-Romero

Christopher J. Savoie

A

Antonio Flores‐Tlacuahuac *

S

Seyed Soheil Mansouri *

DOI：10.1021/acs.iecr.4c04624

Original

Original request for help

Save

Abstract

En 中文

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.

Exploring Hybrid Quantum-Classical Algorithms for Multiscale Bioprocess Optimization

Abstract

Journal

Organization