A quantum active learning algorithm for sampling against adversarial attacks
Abstract
Adversarial attacks represent a serious menace for learning algorithms and may compromise the security of future autonomous systems. A theorem by Khoury and Hadfield-Menell (KH), provides sufficient conditions to guarantee the robustness of active learning algorithms, but comes with a caveat: it is crucial to know the smallest distance among the classes of the corresponding classification problem. We propose a theoretical framework that allows us to think of active learning as sampling the most promising new points to be classified, so that the minimum distance between classes can be found and the theorem KH used. Additionally, we introduce a quantum active learning algorithm that makes use of such framework and whose complexity is polylogarithmic in the dimension of the space, m, and the size of the initial training data n, provided the use of qRAMs; and polynomial in the precision, achieving an exponential speedup over the equivalent classical algorithm in n and m.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2020
- Source ID
- 10.1088/1367-2630/ab976f
Entities
People
- Miguel Ángel Martín-Delgado
- Pablo Antonio Moreno Casares
Organizations
- Army Research Office
- Ministry of Economy, Industry and Competitiveness
- Ministry of Science of Spain