Data scarcity and sparsity in bio-manufacturing poses challenges for accurate modeldevelopment, process monitoring, and optimization. We aim to replicate and capturethe complex dynamics of industrial bioprocesses by proposing the use of a QuantumWasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) togenerate synthetic time series data for industrially relevant processes. Thegenerator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). Thismethodology offers potential advantages in process monitoring, modeling,forecasting, and optimization, enabling more efficient bioprocess management byreducing the dependence on scarce experimental data. Our results demonstrateacceptable performance in capturing the temporal dynamics of real bioprocess data.We focus on Optical Density, a key measurement for Dry Biomass estimation. The datagenerated showed high fidelity to the actual historical experimental data. Thisintersection of quantum computing and machine learning has opened new frontiers indata analysis and generation, particularly in computationally intensive fields, foruse cases such as increasing prediction accuracy for soft sensor design or for usein predictive control.