407 citations · 3.9k across the 91 of their papers we have counts for
6 papers · 1 filter
Quantum Adversarial Machine Learning
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordin…
Experimental Test of Leggett's Inequalities with Solid-State Spins
Xianzhi Huang, Xiaolong Ouyang, Wenqian Lian +9
Bell's theorem states that no local hidden variable model is compatible with quantum mechanics. Surprisingly, even if we release the locality constraint, certain nonlocal hidden va…
Artificial Neural Network Based Computation for Out-of-Time-Ordered Correlators
Yukai Wu, L. -M. Duan, Dong-Ling Deng
Out-of-time-ordered correlators (OTOCs) are of crucial importance for studying a wide variety of fundamental phenomena in quantum physics, ranging from information scrambling to qu…
Adversarial Machine Learning Phases of Matter
Si Jiang, Sirui Lu, Dong-Ling Deng
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on…
Machine Learning Topological Phases with a Solid-state Quantum Simulator
Wenqian Lian, Sheng-Tao Wang, Sirui Lu +11
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. W…
Machine learning meets quantum physics
Sankar Das Sarma, Dong-Ling Deng, Lu-Ming Duan
The marriage of machine learning and quantum physics may give birth to a new research frontier that could transform both.