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Quantum Techniques in Machine Learning (QTML) is an annual international conference focusing on the interdisciplinary field of quantum technology and machine learning. The goal of the conference is to gather leading academic researchers and industry players to interact through a series of scientific talks focussed on the interplay between machine learning and quantum physics.
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), and in Naples (2022). This seventh edition, QTML 2023, will be hosted by CERN, Switzerland.
Example topics include, but are not limited to:
Quantum algorithms for machine learning tasks
Learning and optimisation with hybrid quantum-classical methods
Tensor methods and quantum-inspired machine learning
Data encoding and processing in quantum systems
Machine learning for experimental quantum information
Quantum-enhanced robustness in machine-learning models
Quantum learning theory
Quantum variational circuits
Fuzzy logic for quantum machine learning
Quantum state reconstruction from data
Quantum algorithms for machine-learning tasks
Call for extended abstract & poster submissions
Deadline for abstract & poster submissions
Notification about abstract & poster acceptance
Registration is closed: the event has reached full capacity.