1 hr 12 min
meQuanics - QSI@UTS Seminar Series - S20 - Nana Liu (SJTU) meet the meQuanics - Quantum Computing Discussions
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- Science
During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.
https://youtu.be/7wbK_9Sjnv8
Protecting and leveraging quantum machine learning algorithms on a future quantum internet
TITLE: Introducing Adversarial Quantum Learning: Security and machine learning on the quantum internet
SPEAKER: Assistant Professor Nana Liu
AFFILIATION: Shanghai Jiao Tong University, PR China
HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information
ABSTRACT: In the classical world, there is a powerful interplay between security and machine learning deployed in a network, like on the modern internet. What happens when the learning algorithms and the network itself can be quantum? What are the new problems that can arise and can quantum resources offer advantages to their classical counterparts? We explore these questions in a new area called adversarial quantum learning, that combines the area of adversarial machine learning, which investigates security questions in machine learning, and quantum information. For the first part of the talk, I’ll introduce adversarial machine learning and some exciting potential prospects for contributions from quantum information and computation. For the second part of the talk, I’ll present two new works on adversarial quantum learning. Here we are able to quantify the vulnerability of quantum algorithms for classification against adversaries and learn how to leverage quantum noise to improve its robustness against attacks.
RELATED ARTICLES: Vulnerability of quantum classification to adversarial perturbations: https://arxiv.org/abs/1905.04286Quantum noise protects quantum classifiers against adversaries: https://arxiv.org/abs/2003.09416
OTHER LINKS: nanaliu.weebly.com/
During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.
https://youtu.be/7wbK_9Sjnv8
Protecting and leveraging quantum machine learning algorithms on a future quantum internet
TITLE: Introducing Adversarial Quantum Learning: Security and machine learning on the quantum internet
SPEAKER: Assistant Professor Nana Liu
AFFILIATION: Shanghai Jiao Tong University, PR China
HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information
ABSTRACT: In the classical world, there is a powerful interplay between security and machine learning deployed in a network, like on the modern internet. What happens when the learning algorithms and the network itself can be quantum? What are the new problems that can arise and can quantum resources offer advantages to their classical counterparts? We explore these questions in a new area called adversarial quantum learning, that combines the area of adversarial machine learning, which investigates security questions in machine learning, and quantum information. For the first part of the talk, I’ll introduce adversarial machine learning and some exciting potential prospects for contributions from quantum information and computation. For the second part of the talk, I’ll present two new works on adversarial quantum learning. Here we are able to quantify the vulnerability of quantum algorithms for classification against adversaries and learn how to leverage quantum noise to improve its robustness against attacks.
RELATED ARTICLES: Vulnerability of quantum classification to adversarial perturbations: https://arxiv.org/abs/1905.04286Quantum noise protects quantum classifiers against adversaries: https://arxiv.org/abs/2003.09416
OTHER LINKS: nanaliu.weebly.com/
1 hr 12 min