About
I am Naixu Guo (郭 乃绪), a Ph.D. student at CQT (NUS) advised by Patrick Rebentrost and Miklos Santha. I received my bachelor in 2020 from Kyoto University. Later, I received my master in 2022 from Osaka University, advised by Keisuke Fujii and Kosuke Mitarai.
More information can be seen in the interview by CQT.
Research interests
The polar star of my research direction is the interaction between nature and intelligence. To provide a meaningful scope, I currently focus on the interplay between quantum systems, representing nature, and statistical learning theory and machine learning, embodying intelligence. Some of the topics that I am actively considering are
- Quantum linear algebra and its applications
- Understanding quantum many-body systems via the lens of (theoretical) computer science
Publications
All of my works are also available on my ArXiv and Google Scholar pages.
- Z. Shang, N. Guo, D. An, and Q. Zhao, Design nearly optimal quantum algorithm for linear differential equations via Lindbladians, arxiv: 2410.19628
- N. Guo, Z. Yu, M. Choi, A. Agrawal, K. Nakaji, A. Aspuru-Guzik, and P. Rebentrost, Quantum linear algebra is all you need for Transformer architectures, arxiv:2402.16714 Twi thread PennyLane blog
- L. Zhao, N. Guo, M. Luo, and P. Rebentrost, Provable learning of quantum states with graphical models, arxiv:2309.09235
- N. Guo, F. Pan, and P. Rebentrost, Estimating properties of a quantum state by importance-sampled operator shadows, arxiv:2305.09374
- S. Yang, N. Guo, M. Santha and P. Rebentrost, Quantum Alphatron: quantum advantage for learning with kernels and noise, Quantum
- N. Guo, K. Mitarai and K. Fujii, Nonlinear transformation of complex amplitudes via quantum singular value transformation, Physical Review Research, arxiv:2107.10764
Talks and Conferences
The attached slides only reflect the speaker’s thoughts at that moment and probably consist of errors.
Computational thinking in Quantum
NUS Physics (Wen wei Ho group)
Quantum linear algebra is all you need for transformer architectures
JPMorgan Chase, CQT CS Seminar, RWTH Aachen (Institute for Quantum Information), Free University of Berlin (Jens Eisert group), QTML 2024
Introduction to modern machine learning
Provable learning of quantum states with graphical models
Peking University (Xiao Yuan group), QIP 24 (poster)
Estimating properties of a quantum state by importance-sampled operator shadows
CQT joint Seminar, Westlake University (Wei Zhu group)
Nonlinear transformation of complex amplitudes via quantum singular value transformation