Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring

TL;DR

This paper introduces a Quantum Wasserstein GAN with Gradient Penalty (QWGAN-GP) that uses a Parameterized Quantum Circuit to generate synthetic bioprocess data, addressing data scarcity in industrial bio-manufacturing.

Contribution

The paper presents a novel quantum generative model combining quantum circuits with GANs for realistic synthetic bioprocess data generation.

Findings

Generated data closely matches real bioprocess data.

QWGAN-GP effectively captures temporal dynamics of optical density.

Potential for improved process monitoring and prediction in biomanufacturing.

Abstract

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.