Dr. Xingyao Wu

Xingyao Wu

Professor, School of Computer Science and Technology, Shandong University

About me: I am a tenure-track professor specializing in quantum information science, with core research interests spanning quantum algorithms, circuit optimization, error correction, and physics-informed method. My work bridges the gap between fundamental theoretical physics and applied quantum machine learning, focusing on the scalability of quantum simulations and the architectural design of high-performance quantum software frameworks.

Previously, I led the Quantum AI Lab at JD Explore Academy as a project lead and quantum algorithm scientist, where I spearheaded the design of the open-source quantum machine learning framework, TeD-Q. My academic foundation includes a postdoctoral fellowship at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland, following a Ph.D. from the Centre for Quantum Technologies (CQT) at the National University of Singapore.

Opportunities: The School of Computer Science and Technology is actively recruiting tenured and tenure-track faculty at the intersection of quantum computing and artificial intelligence. Additionally, I am seeking highly motivated Master's and Ph.D. students to join my research group. If you are interested in working together, please reach out to me via the contact section below.

Education

2012.11 — 2017.03
Ph.D. in Quantum Information
Centre for Quantum Technologies (CQT), National University of Singapore

Supervised by Prof. Valerio Scarani. Thesis: Self-testing: Walking on the Quantum Set

2008.09 — 2012.07
B.S. in Theoretical Physics
Special Class for the Gifted Young, University of Science and Technology of China (USTC)

Research Fields

Quantum Algorithms

We study how to achieve practical quantum speedups for real-world applications, with a strong focus on quantum machine learning. Our work develops robust algorithms for chemistry, optimization, and quantum simulation, while actively exploring strategies to secure a relative advantage over classical methods using near-term, noisy intermediate-scale quantum (NISQ) devices.

Physics-Informed Methods

We integrate fundamental physical principles—such as symmetries, conservation laws, and geometric constraints—directly into quantum machine learning models. By embedding these physical priors into our algorithms, we dramatically improve training efficiency, suppress barren plateaus, and ensure highly accurate, physically consistent results when simulating complex quantum systems.

Quantum Error Correction

We design high-performance quantum error-correcting codes and develop advanced, AI-enhanced decoders to combat physical noise. Beyond practical fault-tolerance, our research explores deep theoretical connections between error correction and fundamental physics, investigating how quantum information theory illuminates the nature of black holes, wormholes, and holographic spacetime.

Selected Publications & Patents

Scroll inside the box below to view academic publications and industrial IP disclosures.

Academic Publications

Qiuhao Chen, Yuxuan Du, Yuliang Jiao, Xiliang Lu, Xingyao Wu and Qi Zhao (Corresponding Author)
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
Quantum Science and Technologies, 9 045002 (2024)
Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao
Recent advances for quantum neural networks in generative learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 10 (2023)
Yang Qian, Xinbiao Wang, Yuxuan Du, Xingyao Wu, Dacheng Tao
The Dilemma of Quantum Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 35 4 (2024)
Yuxuan Du, Yang Qian, Xingyao Wu, Dacheng Tao
A distributed learning scheme for variational quantum algorithms
IEEE Transactions on Quantum Engineering, 3 (2022)
Sandesh S Kalantre, Justyna P Zwolak, Stephen Ragole, Xingyao Wu, Neil M Zimmerman, Michael D Stewart, Jacob M Taylor
Machine learning techniques for state recognition and auto-tuning in quantum dots
npj Quantum Information, 5 1 (2019)
Xingyao Wu, Jean-Daniel Bancal, Matthew McKague, Valerio Scarani
Device-independent parallel self-testing of two singlets
Physical Review A, 93 062121 (2016)

Patents & Open Source

Project Lead / First Architect
TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
Open-Source Platform on GitHub, TeD-Q

Contact

Dr. Xingyao Wu

Tenure-Track Professor
School of Computer Science and Technology, Shandong University

Email: wu.x.yao at gmail.com