2020年6月22日，上海交通大学自然科学研究院Nana Liu及其合作者关于对抗性量子学习的最新研究成果在Physical Review A发表。这篇题为“Vulnerability of quantum classification to adversarial perturbations”的论文被编辑遴选为Editor’s Suggestion，作为PRA官网主页的亮点文章 。
On 22th June 2020, Physical Review A published a new research result on adversarial quantum learning by Prof. Nana Liu and her collaborator. This paper, titled ‘Vulnerability of quantum classification to adversarial perturbations’ was selected as Editor’s Suggestion and highlighted on PRA’s official website.
Quantum algorithms that run on quantum computers can be designed for classification problems, like helping machines learn the difference between pictures of ants and cicadas through training examples. They are even more effective for quantum data like quantum states themselves that are created in the laboratory. Machine learning using quantum computers also have more important applications for security, like helping machines learn whether a credit card transaction is fraudulent or legitimate. We might think we are safe with these devices, but what happens when an adversary tries to attack the quantum computer so the quantum learner makes a wrong prediction? How vulnerable are quantum computers to attacks on its machine learning algorithms?
This recent work led by Prof. Nana Liu takes one of the first steps in this new area called adversarial quantum learning. Here a general theoretical bound is proved to show the vulnerability of quantum machine learning devices for classification problems in the presence of attackers. This paper demonstrates that if we know nothing about the data that we want to learn from, then the quantum computer becomes more and more vulnerable to attacks as the size of the data grows. This vulnerability can compromise any speedup advantages that quantum devices can offer. However, if we are given more information about the data we want to classify, then this dependence on the size of the data can greatly diminish. This means we can learn to protect ourselves by doing some of our learning before inputting the data into a quantum computer.