SBIR/STTR Research Award
Quantum Adversarial Machine Learning
Award amount
$225,000Program and phase
SBIR · Phase IAward year
2019Award details
- Awarding agency
- Department of Defense
- Branch
- Office of the Secretary of Defense
- Recipient company
- VCRSOFT LLC
- Proposal award date
- 2019-07-29
- Contract end date
- 2020-01-28
- UEI
- RCB6MVNZ7KM5
- Solicitation number
- 18.3
- Topic code
- SCO183-001
Research abstract
We propose a quantum adversarial machine learning (QAML) approach that combines ideas from different classical AML techniques such as Defense-GAN and thermometer-encoding of inputs. We propose an implementation of Defense-GAN on the D-Wave Leap quantum computing environment. We also propose to leverage ideas from quantum information science such as noisy inputs/outputs/parameters to improve the robustness of the proposed QAML approach. We will use the MNIST dataset to demonstrate feasibility of the proposed approach.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- Strategic Cybersecurity Framework for UTM-ISSA
- Deep Learning for Spacecraft Anomaly Detection
- Secure Edge Computing with Encrypted Neural Networks
- Shipboard Energy Monitoring and Analysis System
- Cloud-Based Predictive Analytics for TBI/PTSD
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- QUINN (Quantum INspired Neural Networks)
- QUINN (Quantum INspired Neural Networks)
- QGAN: Quantum Generative Adversarial Network to Secure Deep Learning
Research funding history
26 SBIR/STTR records share this UEI (2007–2020). Reported award amounts total $4,931,841. Reported amounts are not federal obligations.
Phases: Phase I, Phase II
Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Energy