123 citations · 709 across the 23 of their papers we have counts for
25 papers
A modular quantum computer based on Cornucopia codes
Zhide Lu, Weikang Li, Yinghao Ma +5
Ultra-high-rate quantum error correction codes store many logical qubits in a single code block with few physical qubits. A fault-tolerant quantum computer, however, also requires…
Quantum error correction at ultra-low overhead
Zhide Lu, Weikang Li, Dong-Ling Deng
Suppressing errors is the central challenge for useful large-scale quantum computing. While quantum error correction promises a viable solution to this challenge, existing codes ty…
Cluster-State Witnesses of Finite-Speed Hidden Influences
Weikang Li, Mengyao Hu, Dong-Ling Deng +1
Bell experiments rule out local common-cause explanations of quantum correlations, yet they do not exclude hidden influences that travel faster than light while still having a fini…
Machine learning the arrow of time in solid-state spins
Xiang-Qian Meng, Zhide Lu, Ya-Nan Lu +9
Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversa…
A Unified Frequency Principle for Quantum and Classical Machine Learning
Rundi Lu, Ruiqi Zhang, Weikang Li +3
Quantum neural networks constitute a key class of near-term quantum learning models, yet their training dynamics remain not fully understood. Here, we present a unified theoretical…
Solving excited states for long-range interacting trapped ions with neural networks
Yixuan Ma, Chang Liu, Weikang Li +4
The computation of excited states in strongly interacting quantum many-body systems is of fundamental importance. Yet, it is notoriously challenging due to the exponential scaling…
Demonstration of low-overhead quantum error correction codes
Ke Wang, Zhide Lu, Chuanyu Zhang +39
Quantum computers hold the potential to surpass classical computers in solving complex computational problems. However, the fragility of quantum information and the error-prone nat…
Quantum automated learning with provable and explainable trainability
Qi Ye, Shuangyue Geng, Zizhao Han +3
Machine learning is widely believed to be one of the most promising practical applications of quantum computing. Existing quantum machine learning schemes typically employ a quantu…
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.…
Quantum delegated and federated learning via quantum homomorphic encryption
Weikang Li, Dong-Ling Deng
Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the prote…
Quantum continual learning on a programmable superconducting processor
Chuanyu Zhang, Zhide Lu, Liangtian Zhao +32
Quantum computers may outperform classical computers on machine learning tasks. In recent years, a variety of quantum algorithms promising unparalleled potential to enhance, speed…
Improved Nonlocality Certification via Bouncing between Bell Operators and Inequalities
Weikang Li, Mengyao Hu, Ke Wang +25
Bell nonlocality is an intrinsic feature of quantum mechanics, which can be certified via the violation of Bell inequalities. It is therefore a fundamental question to certify Bell…
Probing many-body Bell correlation depth with superconducting qubits
Ke Wang, Weikang Li, Shibo Xu +32
Quantum nonlocality describes a stronger form of quantum correlation than that of entanglement. It refutes Einstein's belief of local realism and is among the most distinctive and…
Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
Zhihan Zhang, Weiyuan Gong, Weikang Li +1
We study quantum-classical separations between classical and quantum supervised learning models based on constant depth (i.e., shallow) circuits, in scenarios with and without nois…
Non-Abelian braiding of Fibonacci anyons with a superconducting processor
Shibo Xu, Zheng-Zhi Sun, Ke Wang +29
Non-Abelian topological orders offer an intriguing path towards fault-tolerant quantum computation, where information can be encoded and manipulated in a topologically protected ma…
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…
Expressibility-induced Concentration of Quantum Neural Tangent Kernels
Li-Wei Yu, Weikang Li, Qi Ye +3
Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial import…
Enhancing Quantum Adversarial Robustness by Randomized Encodings
Weiyuan Gong, Dong Yuan, Weikang Li +1
The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform th…
Deep quantum neural networks equipped with backpropagation on a superconducting processor
Xiaoxuan Pan, Zhide Lu, Weiting Wang +10
Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of…
Digital simulation of non-Abelian anyons with 68 programmable superconducting qubits
Shibo Xu, Zheng-Zhi Sun, Ke Wang +31
Non-Abelian anyons are exotic quasiparticle excitations hosted by certain topological phases of matter. They break the fermion-boson dichotomy and obey non-Abelian braiding statist…
Quantum Neural Network Classifiers: A Tutorial
Weikang Li, Zhide Lu, Dong-Ling Deng
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has b…
Experimental quantum adversarial learning with programmable superconducting qubits
Wenhui Ren, Weikang Li, Shibo Xu +21
Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning t…
Quantum Capsule Networks
Zidu Liu, Pei-Xin Shen, Weikang Li +2
Capsule networks, which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence. The capsule, as the building block of ca…
Recent advances for quantum classifiers
Weikang Li, Dong-Ling Deng
Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental r…