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Weikang Li

Institute for Interdisciplinary Information Science, Tsinghua University, Beijing 100084, China

4 papers hereh-index 14866 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • quant-ph4
affiliations
  • Institute for Interdisciplinary Information Science, Tsinghua University, Beijing 100084, China
HomepageORCID 0000-0002-7137-5390
same name
  • Weikang Li — 4 papers
  • Weikang Li — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212023
most citedQuantum Neural Network Classifiers: A Tutorial

52 citations · 57 across the 3 of their papers we have counts for

collaborators

4 papers

quant-ph2023

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…

quant-ph2022★ 5 cited

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…

quant-ph2022★ 52 cited

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…

quant-ph2021

Quantum federated learning through blind quantum computing

Weikang Li, Sirui Lu, Dong-Ling Deng

Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the secur…

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