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[2508.21438] Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing
Summary
This article presents a novel framework using quantum-enhanced ensemble GANs for unsupervised anomaly detection in continuous biomanufacturing, demonstrating improved detection rates through hybrid quantum/classical approaches.
Why It Matters
Anomaly detection is critical in continuous biomanufacturing to maintain yield and stability. This research highlights the potential of integrating quantum computing with machine learning techniques, offering a new avenue for enhancing operational efficiency in complex manufacturing processes.
Key Takeaways
- The framework utilizes ensemble GANs for effective anomaly detection.
- A benchmark dataset simulates normal and anomalous operation regimes.
- Hybrid quantum/classical approaches significantly improve detection rates.
- The research addresses the complexities of continuous biomanufacturing processes.
- Findings suggest practical applications of quantum computing in real-world scenarios.
Computer Science > Machine Learning arXiv:2508.21438 (cs) [Submitted on 29 Aug 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing Authors:Rajiv Kailasanathan, William R. Clements, Mohammad Reza Boskabadi, Shawn M. Gibford, Emmanouil Papadakis, Christopher J. Savoie, Seyed Soheil Mansouri View a PDF of the paper titled Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing, by Rajiv Kailasanathan and 6 other authors View PDF HTML (experimental) Abstract: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...
