QTML 2026

10th International Conference on

Quantum Techniques
in Machine Learning

Bridging Quantum Computing and Machine Learning

December 6 - 11, 2026 Jan Mouton Learning Centre, Stellenbosch University
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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.

500+
Attendees
50+
Talks
300+
Posters
5
Days

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.

Important Dates

Mark your calendar with these key deadlines.

📝

Abstract Submission Opens

TBA

Abstract Submission Deadline

TBA

✉️

Notification of Acceptance

TBA

🐦

Early Bird Registration Deadline

TBA

🎉

Conference Dates

December 6 - 11, 2026