23 citations · 62 across the 7 of their papers we have counts for
8 papers
Proving olympiad geometry theorems on a superconducting quantum processor
Ning Wang, Zheng-Zhi Sun, Zhengyi Cui +32
Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide range of applications, and enhanci…
Dynamical freezing and enhanced magnetometry in an interacting spin ensemble
Ya-Nan Lu, Dong Yuan, Yixuan Ma +10
Understanding and controlling non-equilibrium dynamics in quantum many-body systems is a fundamental challenge in modern physics, with profound implications for advancing quantum t…
Observation of topological prethermal strong zero modes
Feitong Jin, Si Jiang, Xuhao Zhu +42
Symmetry-protected topological phases cannot be described by any local order parameter and are beyond the conventional symmetry-breaking paradigm for understanding quantum matter.…
Prethermal Time-Crystalline Corner Modes
Si Jiang, Dong Yuan, Wenjie Jiang +2
We demonstrate the existence of prethermal discrete time crystals whose sub-harmonic response is entirely localized to zero-dimensional corner modes. Within the exponentially long…
Long-lived topological time-crystalline order on a quantum processor
Liang Xiang, Wenjie Jiang, Zehang Bao +38
Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustnes…
Experimental demonstration of adversarial examples in learning topological phases
Huili Zhang, Si Jiang, Xin Wang +7
Classification and identification of different phases and the transitions between them is a central task in condensed matter physics. Machine learning, which has achieved dramatic…
Weighted Quantum Channel Compiling through Proximal Policy Optimization
Weiyuan Gong, Si Jiang, Dong-Ling Deng
We propose a general and systematic strategy to compile arbitrary quantum channels without using ancillary qubits, based on proximal policy optimization -- a powerful deep reinforc…
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…