Important Dates
Early Bird Registration :4th March 2027
Paper Submission Deadline :9th March 2027
Last Date of Registration : 19th March 2027
Date of Conference : 3rd - 4th April 2027
Conference Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals.
SDG 4 — Quality Education
SDG 8 — Decent Work and Economic Growth
SDG 9 — Industry, Innovation and Infrastructure
SDG 12 — Responsible Consumption and Production
SDG 16 — Peace, Justice and Strong Institutions
SDG 17 — Partnerships for the Goals
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.
Track 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.