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QUINN (Quantum INspired Neural Networks)

Award Information
Agency: Department of Defense
Branch: Office of the Secretary of Defense
Contract: HQ003422C0012
Agency Tracking Number: O2-1839
Amount: $1,498,479.95
Phase: Phase II
Program: SBIR
Solicitation Topic Code: SCO183-001
Solicitation Number: 18.3
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2021-12-20
Award End Date (Contract End Date): 2023-12-19
Small Business Information
3600 Green Court Suite 600
Ann Arbor, MI 48105-1111
United States
DUNS: 009485124
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ross Hoehn
 (407) 602-6158
Business Contact
 Denise Nicholson
Phone: (407) 602-6124
Research Institution

QUINN (Quantum INspired Neural Networks) is a cybersecurity implementation to be deployed alongside a machine learning system (especially a reinforcement learning, active learning, and computer vision system) to harden said system from both standard generative adversarial attacks (noise injection) and cyber-physical attacks. This protection is provided by three unique components that perform attack detection and filtering, identification of attack method and affected aspects, and the cleansing of attack-related features/aspects from the data to recover the original un-altered data instance. These capabilities were informed by a Phase I proof-of-purpose that integrated quantum information theory and classical machine learning protocols to conduct attack filtering with a >86% accuracy (optimized) by exploiting information dense qubit-based ensembles, to leverage a quantum information-based identify modification to data and the fingerprints left by the modification method, and to combine state-of-the-art Generative Adversarial Networks combined with qubit-based information distributions to clean incoming data before it can affect the overall machine learning system. Critically, methods outlined in Phase I conduct these procedures in near-real-time (computational cost of less than a second) in a clandestine manner that cannot be detected, characterized, or circumvented by the adversarial attacker.

* Information listed above is at the time of submission. *

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