10th International Conference on
Quantum Techniques
in Machine Learning
Where Quantum Meets Intelligence
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.
From quantum neural networks to variational algorithms, from kernel methods to quantum advantage — explore the research shaping the future of intelligent computing in the quantum era.
Programme Committee
Meet the organising team guiding QTML 2026 and explore the full programme and steering committees.
Amira Abbas
Co-Chair
Adrián Pérez Salinas
Co-Chair
ETH Zürich & Universidad Politécnica de Madrid
Research Topics
Explore the key research areas at the intersection of quantum computing and machine learning.
Quantum Neural Networks
Parameterized quantum circuits as machine learning models, expressibility, trainability, and barren plateaus.
Variational Quantum Algorithms
VQE, QAOA, and other hybrid quantum-classical optimization methods for near-term devices.
Quantum Kernel Methods
Quantum-enhanced feature maps, kernel estimation, and support vector methods on quantum hardware.
Quantum Generative Models
Quantum Boltzmann machines, quantum GANs, born machines, and quantum-inspired generative methods.
Classical ML for Quantum Systems
Using neural networks and classical algorithms for quantum state tomography, characterization, and control.
Quantum Error Correction with ML
Machine learning decoders, fault-tolerant architectures, and ML-guided quantum error mitigation.
Quantum Optimization
Quantum algorithms for combinatorial optimization, portfolio optimization, and logistics problems.
Quantum Natural Language Processing
DisCoCat models, quantum sentence embeddings, and compositional distributional semantics on quantum devices.
Benchmarking & Complexity
Computational advantages of quantum ML, sample complexity, dequantization, and classical simulation limits.
Near-term Applications
Practical quantum ML for chemistry, materials science, finance, drug discovery, and real-world use cases.
Quantum Data Encoding
Encoding and processing classical data in quantum systems — feature maps, data-loading strategies, and embeddings.
Theoretical Foundations of Quantum Learning
Learning theory for quantum models: generalization bounds, expressivity, and the foundations of quantum advantage in learning.
Tensor Networks & Quantum-Inspired ML
Tensor-network methods, matrix product states, and quantum-inspired classical algorithms for machine learning.
Fault-Tolerant Quantum Algorithms
Quantum machine learning algorithms for fault-tolerant, error-corrected devices and provable end-to-end speedups.
Experimental & Hardware QML
Machine learning for experimental quantum information science, hardware demonstrations, and noise-aware methods on real quantum devices.
Important Dates
Mark your calendar with these key deadlines.
Abstract Submission Opens
1 May 2026
Abstract Submission Deadline
7 July 2026 (AoE)
Notification of Acceptance
17 August 2026
Registration Opens
17 August 2026
Early Bird Registration Deadline
TBA
Conference Dates
December 6 - 11, 2026
Co-located Workshop
QTML 2026 will be co-located with the Workshop on Quantum Software (WQS 2026).
Visit WQS 2026 Website