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Opening day
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Submission deadline
Call for Abstracts:
Conference Description
QTML 2026 brings together the brightest minds in quantum computing and machine learning for five days of cutting-edge research, collaboration, and discovery. Hosted for the first time in Stellenbosch, South Africa, the 10th edition of this flagship conference continues the tradition of excellence established across three continents.
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), Singapore (2025).
Submission Guidelines
Papers in the following categories are welcome.
- Talks. Extended abstract 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 research 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
Important Dates:
Paper submission deadline: 30 June 2026
Poster submission deadline: 30 June 2026
