Registration summary
ConferenceICQCML
ModeStandard / Physical
ParticipationListener
Registration fee$155.00
Bank charges (5.8%)$8.99
Total payable$163.99
Includes all bank processing charges — the amount above is exactly what will be charged.
Benefits of Registering as Listener
Access to Conference Sessions
Networking Opportunities
Certificate of Participation
Invitation Letter Support
Conference Kit / Materials
Access to Keynote Sessions
• Conference Session Tracks •
SDG-Aligned Research Themes
ICQCML conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
