Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session Z70: Algorithms for Quantum Annealers and Analog Computers
11:30 AM–2:18 PM,
Friday, March 10, 2023
Room: Room 409
Sponsoring
Unit:
DQI
Chair: Davide Venturelli, NASA QuAIL - USRA
Abstract: Z70.00001 : Privacy-preserving machine learning with tensor networks*
11:30 AM–11:42 AM
Presenter:
Alejandro Pozas-Kerstjens
(Institute of Mathematical Sciences)
Author:
Alejandro Pozas-Kerstjens
(Institute of Mathematical Sciences)
In this talk I will argue and practically illustrate that insights coming from the tensor network representations of quantum many-body states can help in devising better privacy-preserving machine learning algorithms. First, I will show that standard neural networks are vulnerable to a type of privacy leak that, notably, is a priori resistant to the standard protection mechanisms. Then, I will show that tensor networks, when used as machine learning architectures, are invulnerable to this leak. The proof of the resilience is based on the existence of canonical forms for such architectures. Given that tensor networks are recently showing to compete and even surpass traditional machine learning architectures in certain cases, these results imply that one may not have to be forced to make a choice between accuracy in prediction and ensuring the privacy of the information processed when using machine learning on sensitive data.
*This work is supported by the European Union (Horizon 2020 research and innovation programme-grant agreement No. 648913 and ERDF "A way of making Europe"), the Spanish Ministry of Science and Innovation ("Severo Ochoa Programme for Centres of Excellence in R&D" CEX2019-000904-S and ICMAT Severo Ochoa project SEV-2015-0554, and grants CEX2019-000904-S-20-4, MTM2014-54240-P, MTM2017-88385-P, PGC2018-098321-B-I00 and PID2020-113523GB-I00), the Spanish Ministry of Economic Affairs and Digital Transformation (project QUANTUM ENIA, as part of the Recovery, Transformation and Resilience Plan, funded by EU program NextGenerationEU), Comunidad de Madrid (PEJ-2021-AI/TIC-23267 and QUITEMAD-CM P2018/TCS-4342), and the CSIC Quantum Technologies Platform PTI-001.
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