Dr. Ge Bai (柏舸)

Researcher in quantum information

Contact Information

Email: gebai at hkust-gz dot edu dot cn

My Google Scholar page

My publications on arXiv

Download my CV (Last update Sep. 2026)

Personal photo

Now recruiting

I'm now recruiting PhD students, Master students, Research Assistants, Interns, Postdocs at Hong Kong University of Science and Technology (Guangzhou). Students and researchers with a solid mathematics and physics background and a strong interest in quantum information are welcome to join our research group to conduct research in quantum information at AI Thrust, Information Hub, HKUST(GZ).

If you are interested, please send me an email (baige dot research at gmail dot com) with CV, transcript, research experience, and the reason of your application such as your interests. For PhD and Postdoc applications, please also attach a research plan.

(Chinese version here) 柏舸课题组-量子信息全奖博士/硕士/RA/博后招聘，详情请点击此处。

Personal Bio

Ge Bai joined AI Thrust, Information Hub, Hong Kong University of Science and Technology (Guangzhou) in March 2025 as an assistant professor. His research aims to improve the efficiency and reduce the noise of quantum communication, computation and learning, covering topics of quantum causal inference, quantum machine learning, quantum communication network theory, quantum benchmarking, quantum error correction, quantum information theory, etc. Before joining HKUST(GZ), he worked as a postdoctoral fellow at the Centre for Quantum Technologies in the National University of Singapore. He received his Ph.D. from the University of Hong Kong in 2021 and was awarded the Hong Kong Institution of Science Young Scientist Award. His undergraduate study was in Yao Class, Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University. He has published nearly 20 papers in top journals in the field of quantum information such as Physical Review Letters, Nature Communications, IEEE Transactions on Information Theory, npj Quantum Information, Quantum, and has made many oral reports at conferences such as AQIS and Quantum Resources. He is a PC member of AQIS, a reviewer for many top journals and conferences such as Physical Review Letters, QIP, and AQIS.

Research Highlights

A mixed state can represent unspecified ignorance, lack of knowledge of a pure state ("proper mixture"), or discarded entanglement ("improper mixture") — views that yield identical density matrices and identical predictions for future measurements. We show that as prior beliefs for retrodiction (inferring the past from later observations), they lead to different updated beliefs, a purely quantum feature of Bayesian agency. We establish a framework for retrodicting on any quantum belief, prove a necessary and sufficient condition for the equivalence of beliefs, and demonstrate operational consequences in quantum state recovery.

Classically, Bayes' rule can be derived from a principle of minimum change: updated beliefs must be consistent with new data while deviating minimally from the prior. We introduce a quantum analog of this principle, minimizing the change between two quantum input-output processes rather than just their marginals. When the change maximizes the fidelity, the principle has a unique solution, and the resulting quantum Bayes' rule recovers the Petz transpose map in many cases.

Complex processes often arise from sequences of simpler interactions involving a few particles at a time. These interactions, however, may not be directly accessible to experiments. Here we develop the first efficient method for unravelling the causal structure of the interactions in a multipartite quantum process, useful for identifying useful communication channels in quantum networks, and to test the internal structure of uncharacterized quantum circuits.

We develop efficient methods to synthesize energy-conserving unitaries from basic interactions such as XX+YY interaction, showing that generic energy-conserving unitaries like CCZ and Fredkin gates can be implemented with 1 or 2 ancilla qubits depending on the gate set. Our approach supports exact and approximate synthesis, with applications in quantum computing, thermodynamics, and quantum clocks.

Quantum benchmarks are widely used for validating quantum information processing, but practical tests often use finite subsets of input states, leading to artificially higher benchmarks. We demonstrate a setup using one fixed entanglement source to indirectly probe the average fidelity of many states, rigorously validating various protocols.

Publications

Journal Articles

  1. M. Liu, V. Scarani, and G. Bai, “Proper and improper mixed states serve as different prior beliefs for quantum state retrodiction,” Phys. Rev. Lett. 136, 060203 (2026). arXiv:2502.10030
  2. G. Bai, F. Buscemi, and V. Scarani, “A quantum entropy production operator,” J. Phys. A: Math. Theor. 59, 295301 (2026). arXiv:2412.12489
  3. G. Bai, F. Buscemi, and V. Scarani, “Quantum Bayes' rule and Petz transpose map from the minimum change principle,” Phys. Rev. Lett. 135, 090203 (2025). arXiv:2410.00319
  4. G. Bai, D. Šafránek, J. Schindler, F. Buscemi, and V. Scarani, “Observational entropy with general quantum priors,” Quantum 8, 1524 (2024). arXiv:2308.08763
  5. G. Bai and I. Marvian, “Synthesis of energy-conserving quantum circuits with XY interaction,” Quantum Sci. Technol. 9, 045049 (2024). arXiv:2309.11051
  6. F. Shi, G. Bai, X. Zhang, Q. Zhao, and G. Chiribella, “Graph-theoretic characterization of unextendible product bases,” Phys. Rev. Research 5, 033144 (2023). arXiv:2303.02553
  7. Y. D. Wu, Y. Zhu, G. Bai, Y. Wang, and G. Chiribella, “Quantum similarity testing with convolutional neural networks,” Phys. Rev. Lett. 130, 210601 (2023). arXiv:2211.01668
  8. Y. Zhu, G. Bai, Y. Wang, T. Li, and G. Chiribella, “Quantum autoencoders for communication-efficient cloud computing,” Quantum Mach. Intell. 5, 27 (2023). arXiv:2112.12369
  9. Y. Zhu, Y. D. Wu, G. Bai, D. S. Wang, Y. Wang, and G. Chiribella, “Flexible learning of quantum states with generative query neural networks,” Nat. Commun. 13, 6222 (2022). arXiv:2202.06804
  10. G. Bai, Y. D. Wu, Y. Zhu, M. Hayashi, and G. Chiribella, “Quantum causal unravelling,” npj Quantum Inf. 8, 69 (2022). arXiv:2109.13166
  11. Y. D. Wu, G. Bai, G. Chiribella, and N. Liu, “Efficient verification of continuous-variable quantum states and devices without assuming identical and independent operations,” Phys. Rev. Lett. 126, 240503 (2021). arXiv:2012.03784
  12. G. Bai, Y. Yang, and G. Chiribella, “Quantum compression of tensor network states,” New J. Phys. 22, 043015 (2020). arXiv:1904.06772
  13. G. Bai and G. Chiribella, “Test one to test many: a unified approach to quantum benchmarks,” Phys. Rev. Lett. 120, 150502 (2018). arXiv:1711.10240
  14. Y. Yang, G. Bai, G. Chiribella, and M. Hayashi, “Compression for quantum population coding,” IEEE Trans. Inf. Theory 64(7), 4766–4783 (2018). arXiv:1701.03372
  15. X. Yuan, G. Bai, T. Peng, and X. Ma, “Quantum uncertainty relation using coherence,” Phys. Rev. A 96, 032313 (2017).

Conference Papers

  1. J. Chen, C. Zhu, G. Bai, and X. Wang, “Triage: an adaptive parallel window decoding scheduler for real-time fault-tolerant quantum computation,” to appear in Proc. 53rd IEEE/ACM International Symposium on Computer Architecture (ISCA), 2026. arXiv:2605.04459
  2. G. Bai, I. Damgård, C. Orlandi, and Y. Xia, “Non-interactive verifiable secret sharing for monotone circuits,” AFRICACRYPT 2016, pp. 225–244, 2016.
  3. G. Bai, H. Mou, Y. Hou, Y. Lyu, and W. Yang, “Android power management and analyses of power consumption in an Android smartphone,” IEEE HPCC 2013, 2013.

Preprints

  1. F. Shi, G. Bai, X. Zhang, Q. Zhao, and L. Li, “A minimum-cardinality genuinely unextendible product basis in three qutrits,” arXiv:2608.12785 (2026).
  2. K. He, Z. Tang, Z. Li, G. Bai, and X. Wang, “Logical entangling with phantom codes in hypergraph products,” arXiv:2607.12948 (2026).
  3. C. Zhu, Z. Tang, G. Zhen, Y. Li, G. Bai, and X. Wang, “Simulation of adjoints and Petz recovery maps for unknown quantum channels,” arXiv:2602.05828 (2026).
  4. M. Liu, G. Bai, and V. Scarani, “Unifying quantum smoothing theories with extended retrodiction,” arXiv:2510.08447 (2025).
  5. G. Bai, “Bayesian retrodiction of quantum supermaps,” arXiv:2408.07885 (2024).