Progress in Quantum Machine Learning Workshop
Progress in Quantum Machine Learning is a one-day international workshop designed to advance the future of quantum‑enabled AI.
Join leading researchers from the international community for a focused, high‑impact workshop exploring how quantum technologies are reshaping machine learning, materials discovery, particle physics, drug design, and more.
The workshop is part of the celebrations of Tsinghua University’s Department of Physics 100th anniversary and is being arranged in conjunction with IOP Publishing’s journal Reports on Progress in Physics (ROPP), which recently celebrated its 90th birthday and is now open for original research submissions.
Why Attend?
- Get ahead in one of the fastest‑moving fields in science
- Hear directly from global leaders
- Grow your network with cross‑disciplinary experts
- Showcase your work through our poster session
Register now
Attend online or in-person, select your registration preference by clicking below:
When
10 May 2026
Format
1 day event
Venue
Tsinghua University, Beijing, China and online
Address: No. 30, Shuangqing Road, Haidian District, Beijing, China
Chair
Professor Wenhui Duan
Tsinghua University
“As we embark on our next century, we are proud to announce a strategic partnership with IOP Publishing. Together, we aim to foster global dialogue, accelerate innovation, and define the future of physics on the world stage.”
Co-chairs and Event Organisers
Professor Xiaoming Sun
Institute of Computing Technology, Chinese Academy of Sciences (CAS)
Professor Xiao Yuan
Peking University
Professor Pan Zhang
Institute of Theoretical Physics, CAS
Professor Lei Wang
Institute of Physics, CAS
Professor Di Luo
Tsinghua University
Dr David Gevaux
Chief Editor, ROPP
Chloe Wu
Editorial Development Manager, IOP Publishing
Emmie Yang
Head of Publishing, APAC, IOP Publishing
Call for posters
Showcase your latest research with a global quantum ML community. This is your opportunity to gain visibility, spark new collaborations, and contribute to one of the most rapidly advancing areas of science.
Why submit a poster?
- Present your work to an international community of experts
- Receive constructive feedback from members of the Tsinghua review committee
- Build valuable connections during our dedicated poster session
- Highlight your contributions to domains such as quantum computing, materials discovery, physics informed ML, and more
How to submit
- Email your abstract and a PDF version of your poster to shuyanliu@tsinghua.edu.cn
- Use the subject line: “Poster for Progress in Quantum Machine Learning Workshop”
- Deadline: 15 April 2026
Poster size: 90 cm (width) × 120 cm (height). Please bring a printed version to the venue.
Plenary Speakers
Qikun Xue
Southern University of Science and Technology
Mauro Paternostro
Queen’s University Belfast, United Kingdom
Wolfgang Mauerer
Technical University of Applied Science Regensburg, Germany
Alexey Melnikov
Terra Quantum AG , Switzerland
Dongling Deng
Tsinghua University
Shengyu Zhang
Tencent
Shi Jin
Shanghai Jiaotong University
Chaoyang Lu
University of Science and Technology of China
Lirandë Pira
National University of Singapore
Supporting Journals
Reports on Progress in Physics (ROPP)
Part of the Progress In series, Reports on Progress in Physics (ROPP) is a highly selective multidisciplinary journal with a mission to publish ground-breaking original research and authoritative reviews of the highest quality and significance, across all areas of physics and related areas.
Explore our Research Highlights for quick, accessible summaries of the latest impactful research published across the Progress In journal series.
Impact Factor: 20.7
Citescore: 31.0
Quantum Science and Technology
Quantum Science and Technology (QST) is a multidisciplinary, high impact journal devoted to publishing both theoretical and experimental research of the highest quality and significance covering the science and application of all quantum-enabled technologies.
Impact Factor: 5.0
Citescore: 10.9
Machine Learning: Science and Technology
Machine Learning: Science and Technology (MLST) is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights.
Impact Factor: 4.6
Citescore: 7.7
