NASA · NASA-13 · record R0110

Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

National Aeronautics and Space Administration · ARC: Ames Research Center

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

anomaly detection

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: in-flight operations data streams
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported