Yuxuan (Vincent) Zhang
PH H1 477,
1015 Lausanne
Switzerland
Hello! This is Yuxuan (Vincent) Zhang (张宇轩). I am currently a postdoctoral researcher in Prof. Dmitry Abanin’s group, superposed between Princeton and EPFL.
My research lies at the interface of quantum information, quantum many-body physics, and artificial intelligence. I am strongly motivated by questions such as: How can we achieve verifiable quantum advantage on near-term hardware? How can ideas from many-body physics advance quantum computation, and how can quantum computers help us better understand the laws of nature? And how can we best use machine-learning tools to improve the design of quantum computers?
Previously, I was a CQIQC Fellow at the Centre for Quantum Information and Quantum Control at the University of Toronto, with a joint appointment at the Vector Institute for Artificial Intelligence, where I worked closely with Yong-Baek Kim, Juan Carrasquilla (now at ETHz), and Dvira Segal.
I obtained my Ph.D. in Physics from The University of Texas at Austin, where I was fortunate to be mentored by Andrew C. Potter (now at Quantinuum) and Scott Aaronson. During my Ph.D., I became deeply interested in quantum information, quantum matter, and the computational structure of physical systems. Before that, I received my B.S. in Physics with Highest Honors from the University of California, Santa Barbara in 2016, and then spent a year at the Institute of High Energy Physics in Beijing, working on collider physics and detector reconstruction.
Outside research, I enjoy traveling, photography, classical music, and real-time strategy games.
After all, what have I learned about the quantum world so far? Well, in short:
You observed me, and thus we entangled — though I could never be a copy of you.
news
| Aug 2026 | The group opens at NUS in 2027 and I am hiring: two postdoctoral researchers and PhD students, in Computer Science and in Physics. If verifiable quantum advantage, quantum many-body physics, or machine learning for quantum computers is your thing, see joining the group for deadlines and how to apply. |
|---|---|
| Aug 2026 | Heuristic Quantum Advantage with Peaked Circuits was named #1 Best Paper of the 56 in the Quantum Algorithms track at IEEE Quantum Week (QCE) 2026. A 2000-gate instance that Quantinuum’s H2 solves in under two hours extrapolates to years on Frontier — and unlike sampling claims, the answer is checkable. With the team at BlueQubit. |
| Jun 2026 | How do you tell which mixed-state phase a noisy quantum computer is actually in? With Timothy Hsieh and Yijian Zou at Perimeter and Yong Baek Kim at Toronto, we answered it on Quantinuum’s H1 with Rényi correlators and a variational decoder — now out in Nature Communications. |
| Jun 2026 | Delighted to share that I will join the National University of Singapore as an Assistant Professor in December 2026, jointly appointed in Computer Science and Physics, where I will start a group on quantum information, quantum many-body physics, and AI. |
| May 2026 | Can a quantum computer learn how to compile its own dynamics? Scalable quantum dynamics compilation via quantum machine learning is out in Physical Review Research — with Roeland Wiersema (Vector & Waterloo), Juan Carrasquilla (ETH Zürich), Lukasz Cincio (Los Alamos) and Yong Baek Kim (Toronto). |
selected publications
2026
- Probing mixed-state phases on a quantum computer via Renyi correlators and variational decodingNature Communications, 2026
- Scalable quantum dynamics compilation via quantum machine learningPhysical Review Research, 2026
- Biorthogonal Neural Network Approach to Two-Dimensional Non-Hermitian SystemsPhysical Review Letters, 2026
2025
- Classical Simulability of Quantum Circuits with Shallow Magic DepthPRX Quantum, 2025Selected for the International Year of Quantum Collection
- Observation of a non-Hermitian supersonic mode on a trapped-ion quantum computerNature Communications, 2025
2024
2023
2022
- Holographic simulation of correlated electrons on a trapped-ion quantum processorPRX Quantum, Apr 2022