# Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing

## TL;DR

This paper introduces a quantum-enhanced ensemble GAN framework for early anomaly detection in complex continuous biomanufacturing, demonstrating improved detection rates with hybrid quantum/classical methods.

## Contribution

It presents a novel hybrid quantum/classical ensemble GAN approach and establishes a benchmark dataset for anomaly detection in biomanufacturing.

## Key findings

- Hybrid quantum/classical GAN improves anomaly detection performance.
- Benchmark dataset effectively simulates process anomalies.
- Quantum approach shows potential for real-world biomanufacturing applications.

## 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 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.