Quantum Computing for Synthetic Bioprocess Data Generation and Time-Series Forecasting

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Abstract

Data scarcity in pilot-scale photobioreactors, particularly for single-cell organism cultivation, poses challenges for accurate model development and process optimization. We propose using a quantum Generative Adversarial Network (GAN) to generate synthetic time-series data, with a focus on Optical Density, a key metric for Dry Biomass estimation. Our results show high fidelity in synthetic data generation. This approach addresses critical data gaps, enabling better model development and parameter optimization in bioprocess engineering.
Original languageEnglish
Title of host publicationESCAPE 35: 35th European Symposium on Computer Aided Process Engineering 2025 : Book of Short Papers
Editors Jan F. M. Van Impe, Grégoire Léonard, Satyajeet S. Bhonsale, Monika E. Polanska, Filip Logist
PublisherEUROSIS
Publication date2025
Pages95-96
Article number1351
ISBN (Electronic)978-9-492859-36-5
Publication statusPublished - 2025
Event35th European Symposium on Computer Aided Process Engineering (ESCAPE 35) - KU Leuven Campus Ghent, Ghent, Belgium
Duration: 6 Jul 20259 Jul 2025

Conference

Conference35th European Symposium on Computer Aided Process Engineering (ESCAPE 35)
LocationKU Leuven Campus Ghent
Country/TerritoryBelgium
CityGhent
Period06/07/202509/07/2025
SeriesSystems & Control Transactions
Volume4
ISSN2818-4734

Keywords

  • Quantum machine learning
  • Synthetic data generation
  • Bioprocess time series prediction
  • Generative adversarial networks

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