QTML 2025: 9th International Conference on Quantum Techniques in Machine Learning Singapore, Singapore, November 16-21, 2025

Theme for 2025: AI for Quantum, Quantum for AI

Conference description and historique

Quantum Techniques in Machine Learning (QTML) is a leading international conference at the forefront of quantum science and machine learning. Held annually, it brings together researchers and industry experts to explore how quantum computing can transform learning, optimization, and data-driven discovery. Through a series of scientific talks and discussions, QTML fosters collaboration and advances research on the interplay between quantum mechanics and machine learning, from foundational theory to real-world applications.

QTML was first hosted in Verona, Italy (2017), then in Durban, South Africa (2018),  Daejeon, South Korea (2019), virtual (2020, hosted by Zapata Computing), virtual (2021, hosted by RIKEN-AIP), Naples (2022), CERN (2023), Melbourne (2024).

The ninth edition, QTML 2025, will be hosted by the Centre for Quantum Technologies in Singapore.

Submission guidelines

Papers in the following categories are welcome.

• Extended abstracts. Work describing original results or summarizing already published works must be submitted in PDF format, single column, single-space, 11-point fonts, maximum length of 3 pages (excluding references). Accepted abstracts will be presented at the conference as short or long talks.
• Posters. One-page abstracts describing the work to be exhibited as a poster must be submitted in PDF format, single column, single-space 11-point fonts. 

Conference topics

Contributions are welcome in all reasearch areas covering the application of quantum techniques for machine learning and optimization tasks as well as the use of machine learning algorithms for studying quantum systems.

We welcome contributions on a broad range of topics, including and not limited to: 

• Quantum algorithms for machine learning applications
• Hybrid quantum-classical approaches for learning and optimization
• Encoding and processing of data in quantum systems
• Theoretical foundations of quantum learning
• Machine learning techniques for experimental quantum information science
• Quantum-enhanced robustness in machine learning models
• Tensor methods and quantum-inspired machine learning
• Quantum variational circuits and their applications
• Fuzzy logic in quantum machine learning
• Quantum state reconstruction from data
• Quantum state and process tomography with learning-based approaches
• Quantum kernel methods and their applications.

Committees

Steering Committee

• Alessandra Di Pierro, Università di Verona, Italy
• Francesco Petruccione, Stellenbosch University, South Africa
• June-Koo Kevin Rhee, KAIST, South Korea
• Michele Grosso, CERN, Switzerland
• Giovanni Acampora, Università di Napoli, Italy
• Muhammad Usman, CSIRO and University of Melbourne, Australia

Organising Committee

• Patrick Rebentrost (co-chair), CQT, National University of Singapore 
• Lirandë Pira (co-chair), CQT, National University of Singapore
• Marco Tomamichel, CQT, National University of Singapore 
• Yvonne Gao, CQT, National University of Singapore 
• Valerio Scarani, CQT, National University of Singapore 

Program Committee (TBA)

• Maria Schuld (co-chair), Xanadu, Canada
• Ryan Sweke (co-chair), African Institute for Mathematical Sciences (AIMS), South Africa