# Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing

arXiv: https://arxiv.org/abs/2508.21438 · HTML: https://mlsanity.com/paper/2508.21438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Quantum Generative Models for Small Molecule Drug Discovery](https://mlsanity.com/paper/2101.03438/) (2021)
- [DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN](https://mlsanity.com/paper/1808.07632/) (2018)
- [GAN Ensemble for Anomaly Detection](https://mlsanity.com/paper/2012.07988/) (2020)
- [Complexity Matters: Rethinking the Latent Space for Generative Modeling](https://mlsanity.com/paper/2307.08283/) (2023)
- [Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection](https://mlsanity.com/paper/2310.05307/) (2023)
- [Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning](https://mlsanity.com/paper/1904.10573/) (2019)
- [Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems](https://mlsanity.com/paper/2003.00264/) (2020)
- [Deep Anomaly Detection on Tennessee Eastman Process Data](https://mlsanity.com/paper/2303.05904/) (2023)
- [Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment](https://mlsanity.com/paper/2508.16297/) (2025)
- [The Classical Complexity of Boson Sampling](https://mlsanity.com/paper/1706.01260/) (2017)
- [Exact gradients for linear optics with single photons](https://mlsanity.com/paper/2409.16369/) (2024)
- [Quantum latent distributions in deep generative models](https://mlsanity.com/paper/2508.19857/) (2025)
- [Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process](https://mlsanity.com/paper/2403.13502/) (2024)

## Abstract (arXiv)

The development of continuous biomanufacturing processes requires robust and early anomaly detection, since even minor deviations can compromise yield and stability, leading to disruptions in scheduling, reduced weekly production, and diminished economic performance. These processes are inherently complex and exhibit non-linear dynamics with intricate relationships between process variables, thus making advanced methods for anomaly detection essential for efficient operation. In this work, we present a novel framework for unsupervised anomaly detection in continuous biomanufacturing based on an ensemble of generative adversarial networks (GANs). We first establish a benchmark dataset simulating both normal and anomalous operation regimes in a continuous process for the production of a small molecule. We then demonstrate the effectiveness of our GAN-based framework in detecting anomalies caused by sudden feedstock variability. Finally, we evaluate the impact of using a hybrid quantum/classical GAN approach with both a simulated quantum circuit and a real photonic quantum processor on anomaly detection performance. We find that the hybrid approach yields improved anomaly detection rates. Our work shows the potential of hybrid quantum/classical approaches for solving real-world problems in complex continuous biomanufacturing processes.