Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model
Description
Our team developed high-performance unsupervised deep machine-learning models for the detection of flight-operations anomalies. The models’ engineered-feature (latent) spaces are composed of discrete variables, which allows an integration with quantum computing because (part of) the latent-space variables can be populated by quantum-state measurements, which are discrete in nature.
Detailed example
anomaly detection
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
This project enabled the additional development of two quantum-capable unsupervised deep-learning models with discrete latent space (Bernoulli and Boltzmann priors). The models exhibit state-of-the-art anomaly-detection performance and robustness. Future versions of our models will be deployed on in-time flight-operations data streams. They will also be used to assess the performance and resource requirements of quantum and other physical computing devices.
Controls / human review
ATO: No; PIA: Not published
Data needed
in-flight operations data streams