PeopleofSuperintelligence

Researcher

Qiaoyan Wen

Also known as Qiao-Yan Wen, 温巧燕

Qiaoyan Wen is a professor at Beijing University of Posts and Telecommunications whose work joins quantum cryptography with quantum machine learning. In 2008, they coauthored security analyses of quantum secret-sharing and quantum dialogue protocols, and in 2017 they coauthored an improved quantum algorithm for ridge regression that IEEE Transactions on Knowledge and Data Engineering published in 2021. Quantum models for learning followed: a quantum conditional random field in 2019, a quantum restricted Boltzmann machine in 2020, a quantum federated learning framework for classical clients in 2023, and the RE-QCNN convolutional network in 2024. Their 2023 quantum AES circuits set a new qubit-count record, and in 2025 they coauthored QD-LLM, a quantum knowledge distillation model for large language models. Their group works on quantum cryptography and quantum algorithms.

Timeline

  1. 2008

    Cryptanalysis of quantum protocols

    In January 2008, they coauthor two security analyses of quantum communication protocols with Fei Gao and Fu-Chen Zhu. One, with Su-Juan Qin, shows that the Hillery-Buzek-Berthiaume quantum secret-sharing protocol is insecure against dishonest participants; the other, with Fen-Zhuo Guo, finds that two quantum dialogue protocols and a bidirectional quantum secure direct communication protocol leak part of the transmitted information.

  2. 2017–2021

    Quantum ridge regression

    In 2017, they coauthor an improved quantum algorithm for ridge regression with Chao-Hua Yu and Fei Gao. It uses parallel Hamiltonian simulation to build a quantum version of K-fold cross-validation and reaches an exponential speedup over the classical method for low-rank data matrices with low condition numbers. IEEE Transactions on Knowledge and Data Engineering publishes the paper in 2021.

  3. 2019–2020

    Quantum CRF and Boltzmann machines

    In January 2019, they coauthor a quantum conditional random field with Yusen Wu, Chao-Hua Yu, Binbin Cai, Sujuan Qin and Fei Gao, together with a quantum training algorithm that has an exponential speed-up over its classical counterpart. In 2020, they coauthor a quantum restricted Boltzmann machine that is proved universal for quantum computation and can be implemented on noisy intermediate-scale quantum devices.

  4. 2020

    Quantum cryptography and algorithms group

    In October 2020, their team at Beijing University of Posts and Telecommunications' School of Cyberspace Security, the quantum cryptography and quantum algorithms group, recruits postdoctoral researchers. The team's projects include a National Natural Science Foundation of China grant for quantum algorithms for machine-learning problems, running from 2020 to 2023.

  5. 2023

    Fewer qubits for quantum AES

    In 2023, they and Zhenqiang Li, Fei Gao and Sujuan Qin build quantum circuits of AES-128, AES-192 and AES-256 with 264, 328 and 392 qubits, a new record, published in Frontiers in Physics in March 2023. Strategic Study of CAE carries their 2023 review of quantum network systems with Jing Li, Fei Gao, Sujuan Qin and Ping Zhang, which sorts quantum networks into quantum cryptography, quantum cloud computing and quantum teleportation networks.

  6. 2023–2024

    Quantum federated learning and RE-QCNN

    In December 2023, they coauthor CC-QFL, a quantum federated learning framework for classical clients in which shadow tomography assists the collaborative training of the quantum machine learning model. In April 2024, Frontiers in Physics publishes RE-QCNN, a resource-efficient quantum convolutional neural network they coauthor with Yanqi Song, Jing Li, Yusen Wu, Sujuan Qin and Fei Gao.

  7. 2025

    QD-LLM for language models

    In May 2025, they coauthor QD-LLM, a quantum knowledge distillation model for large language models built on variational quantum circuits. In classical simulation, it outperforms several mainstream distillation methods on text classification tasks using only 11 qubits, and the authors run its circuits on the Baihua superconducting quantum processor through the Quafu platform.

  8. As of October 2026

    Professor at BUPT

    As of October 2026, they are a professor at Beijing University of Posts and Telecommunications. In 2025, their affiliations include the State Key Laboratory of Networking and Switching Technology and the National Engineering Research Center of Disaster Backup and Recovery.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Beijing University of Posts and Telecommunications. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Beijing University of Posts and Telecommunications. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Beijing University of Posts and Telecommunications. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Beijing University of Posts and Telecommunications. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Sources and credits

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  1. [0801.2418] Cryptanalysis of the Hillery-Buzek-Berthiaume quantum secret-sharing protocol arxiv.org. Checked 22 October 2026.
  2. [0801.2420] Revisiting the security of quantum dialogue and bidirectional quantum secure direct communication arxiv.org. Checked 22 October 2026.
  3. [1707.09524] An improved quantum algorithm for ridge regression arxiv.org. Checked 22 October 2026.
  4. 温巧燕-北京邮电大学|科研之友 scholarmate.com. Checked 22 October 2026.
  5. [1901.01027] Quantum Conditional Random Field arxiv.org. Checked 22 October 2026.
  6. [2005.11970] Quantum restricted Boltzmann machine universal for quantum computation arxiv.org. Checked 22 October 2026.
  7. 科学网－北京邮电大学网络空间安全学院温巧燕教授团队（量子密码与量子算法组）招聘博士后人员 talent.sciencenet.cn. Checked 22 October 2026.
  8. Frontiers | New record in the number of qubits for a quantum implementation of AES frontiersin.org. Checked 22 October 2026.
  9. New record in the number of qubits for a quantum implementation of AES eprint.iacr.org. Checked 22 October 2026.
  10. Research Progress and Key Technologies of Quantum Network Systems engineering.org.cn. Checked 22 October 2026.
  11. [2312.11672] A Quantum Federated Learning Framework for Classical Clients arxiv.org. Checked 22 October 2026.
  12. Frontiers | A resource-efficient quantum convolutional neural network frontiersin.org. Checked 22 October 2026.
  13. [2505.13205] Quantum Knowledge Distillation for Large Language Models arxiv.org. Checked 22 October 2026.
  14. Quantum Knowledge Distillation for Large Language Models arxiv.org. Checked 22 October 2026.