A LITTLE ABOUT ME
Weikang Li 李炜康
At the meeting point of quantum physics and machine learning.
01 /Hello, I'm Weikang.
I am Weikang Li (李炜康), a physicist working on quantum information and machine learning. I received my Ph.D. in Physics from Tsinghua University in 2025, advised by Prof. Dong-Ling Deng at the Center for Quantum Information, IIIS. Before that, I studied Applied Physics at the School of the Gifted Young, USTC, receiving my B.S. in 2020.
02 /Research interests
- Quantum computation & quantum information
- Quantum machine learning
- Quantum error correction
- Artificial intelligence
03 /Publications
* Equal contribution · # Corresponding author · WKL = Weikang Li
Preprints 6 papers
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Electrostatic limits on quantum spatial search
arXiv:2610.23560 (2026)
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A modular quantum computer based on Cornucopia codes
arXiv:2610.19073 (2026)
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Proving olympiad geometry theorems on a superconducting quantum processor
arXiv:2609.14533 (2026)
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Cluster-state witnesses of finite-speed hidden influences
arXiv:2608.05271 (2026)
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Quantum error correction at ultra-low overhead
arXiv:2608.02773 (2026)
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A unified frequency principle for quantum and classical machine learning
arXiv:2601.03169 (2026)
Journals 23 papers
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Improved certification of Bell-operator correlations via optimization
Physical Review A, in press (2026)
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Quantum automated learning
Nature Communications, in press (2026)
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Solving excited states for long-range interacting trapped ions with neural networks
Science Bulletin 71, 3881 (2026)
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Demonstration of low-overhead quantum error correction codes
Nature Physics 22, 308 (2026)
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Experimental quantum continual learning with superconducting qubits
npj Quantum Information 12, 28 (2026)
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Pitfalls and prospects of quantum machine learning
Nature Computational Science 5, 1095 (2025)
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Topological prethermal strong zero modes on superconducting processors
Nature 645, 626 (2025)
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Probing many-body Bell correlation depth with superconducting qubits
Physical Review X 15, 021024 (2025)
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Quantum delegated and federated learning via quantum homomorphic encryption
Research Directions: Quantum Technologies 3, e3 (2025)
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Extracting reliable quantum outputs for noisy devices
Nature Computational Science 4, 811 (2024)
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Long-lived topological time-crystalline order on a quantum processor
Nature Communications 15, 8963 (2024)
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Expressibility-induced concentration of quantum neural tangent kernels
Reports on Progress in Physics 87, 110501 (2024)
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Quantum-classical separations in shallow-circuit-based learning with and without noises
Communications Physics 7, 290 (2024)
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Non-Abelian braiding of Fibonacci anyons with a superconducting processor
Nature Physics 20, 1469 (2024) (Cover Story)
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Enhancing quantum adversarial robustness by randomized encodings
Physical Review Research 6, 023020 (2024)
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Deep quantum neural networks on a superconducting processor
Nature Communications 14, 4006 (2023)
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Digital simulation of projective non-Abelian anyons with 68 superconducting qubits
Chinese Physics Letters 40, 060301 (2023) (Express Letter & Cover Story)
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Quantum capsule networks
Quantum Science and Technology 8, 015016 (2022)
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Experimental quantum adversarial learning with programmable superconducting qubits
Nature Computational Science 2, 711 (2022) (Cover Story)
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Quantum neural network classifiers: a tutorial
SciPost Physics Lecture Notes 61 (2022)
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Recent advances for quantum classifiers
SCPMA 65, 220301 (2022)
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Quantum federated learning through blind quantum computing
SCPMA 64, 100312 (2021)
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Adversarial learning in quantum artificial intelligence
Acta Physica Sinica 70, 140302 (2021)