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International Conference on Quantum Computing and Machine Learning

ICQCML

26th May – 27th May 2027 Maribor, Slovenia

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

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Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

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Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 4 SDG 8 SDG 9 SDG 12
01 Quantum Algorithms for Machine Learning +
This track focuses on the development and analysis of quantum algorithms specifically designed for machine learning tasks. Contributions may include novel approaches that leverage quantum principles to enhance computational efficiency and accuracy.
SDG 9 SDG 17
02 Quantum Neural Networks: Theory and Applications +
This session explores the theoretical foundations and practical implementations of quantum neural networks. Researchers are invited to present innovative architectures and their applications in solving complex problems.
SDG 4 SDG 9
03 Quantum Optimization Techniques in Machine Learning +
This track addresses the integration of quantum optimization methods within machine learning frameworks. Papers should discuss how quantum techniques can improve optimization processes in training machine learning models.
SDG 8 SDG 9
04 Quantum-Enhanced Learning Paradigms +
This session investigates the impact of quantum computing on various learning paradigms, including supervised and unsupervised learning. Contributions should highlight the advantages of quantum-enhanced approaches over classical methods.
SDG 4 SDG 9
05 Quantum Data Analysis and Feature Extraction +
This track focuses on methodologies for analyzing quantum data and extracting relevant features for machine learning applications. Submissions should present novel techniques that exploit quantum properties for improved data insights.
SDG 9 SDG 12
06 Hybrid Quantum-Classical Models in AI +
This session explores the development of hybrid models that combine quantum and classical computing techniques in artificial intelligence. Researchers are encouraged to present case studies demonstrating the effectiveness of such models.
SDG 9 SDG 17
07 Reinforcement Learning in Quantum Systems +
This track examines the intersection of reinforcement learning and quantum systems. Papers should focus on novel algorithms and their applications in environments that leverage quantum mechanics.
SDG 4 SDG 9
08 Quantum Classification and Predictive Modeling +
This session highlights advancements in quantum classification techniques and their applications in predictive modeling. Contributions should demonstrate how quantum methods can enhance classification accuracy and model performance.
SDG 9 SDG 12
09 Anomaly Detection Using Quantum Techniques +
This track focuses on the application of quantum computing for anomaly detection in various datasets. Researchers are invited to present innovative solutions that utilize quantum algorithms to identify outliers effectively.
SDG 9 SDG 16
10 Deep Learning Integration with Quantum Computing +
This session investigates the integration of deep learning methodologies with quantum computing frameworks. Contributions should explore how quantum resources can enhance deep learning architectures and processes.
SDG 9 SDG 12
11 Quantum Simulation for Machine Learning Applications +
This track examines the role of quantum simulation in advancing machine learning applications. Papers should discuss how quantum simulations can provide insights and improve the performance of machine learning models.
SDG 9 SDG 12