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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.17688 |
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| _version_ | 1866909858574368768 |
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| author | Gibford, Shawn M. Boskabadi, Mohammad Reza Savoie, Christopher J. Mansouri, Seyed Soheil |
| author_facet | Gibford, Shawn M. Boskabadi, Mohammad Reza Savoie, Christopher J. Mansouri, Seyed Soheil |
| contents | Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model
development, process monitoring, and optimization. We aim to replicate and capture
the complex dynamics of industrial bioprocesses by proposing the use of a Quantum
Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) to
generate synthetic time series data for industrially relevant processes. The
generator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). This
methodology offers potential advantages in process monitoring, modeling,
forecasting, and optimization, enabling more efficient bioprocess management by
reducing the dependence on scarce experimental data. Our results demonstrate
acceptable performance in capturing the temporal dynamics of real bioprocess data.
We focus on Optical Density, a key measurement for Dry Biomass estimation. The data
generated showed high fidelity to the actual historical experimental data. This
intersection of quantum computing and machine learning has opened new frontiers in
data analysis and generation, particularly in computationally intensive fields, for
use cases such as increasing prediction accuracy for soft sensor design or for use
in predictive control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring Gibford, Shawn M. Boskabadi, Mohammad Reza Savoie, Christopher J. Mansouri, Seyed Soheil Emerging Technologies Machine Learning Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model development, process monitoring, and optimization. We aim to replicate and capture the complex dynamics of industrial bioprocesses by proposing the use of a Quantum Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) to generate synthetic time series data for industrially relevant processes. The generator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). This methodology offers potential advantages in process monitoring, modeling, forecasting, and optimization, enabling more efficient bioprocess management by reducing the dependence on scarce experimental data. Our results demonstrate acceptable performance in capturing the temporal dynamics of real bioprocess data. We focus on Optical Density, a key measurement for Dry Biomass estimation. The data generated showed high fidelity to the actual historical experimental data. This intersection of quantum computing and machine learning has opened new frontiers in data analysis and generation, particularly in computationally intensive fields, for use cases such as increasing prediction accuracy for soft sensor design or for use in predictive control. |
| title | Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring |
| topic | Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2510.17688 |