Special Session: Quantum Machine Learning Algorithms and Applications
IJCNN 2025 Special Session
Call for Papers
In recent years, quantum computing has made remarkable strides, evolving from theoretical concepts introduced in the 1980s to hardware prototypes in the 2020s capable of handling hundreds of qubits. While still in its early stages, the rapid progress in quantum hardware and algorithm development has sparked discussions about the potential for Noisy-Intermediate Scale Quantum (NISQ) devices to outperform classical computing systems in specific domains. Among the most promising approaches designed for NISQ devices are Variational Quantum Algorithms (VQAs) and the Variational Quantum Eigensolver (VQE). These algorithms exhibit resilience to noise and are well-suited for operation on devices with limited qubit resources. Additionally, Hamiltonian simulations offer profound insights into quantum system dynamics, broadening the scope of quantum computing applications to real-world challenges, including signal processing. This expanding suite of quantum tools holds the potential to revolutionize signal processing, paving the way for groundbreaking research and practical advancements.
This Special Session invites submissions that delve into interdisciplinary innovations and address emerging challenges in neural networks and computational intelligence, with a strong focus on groundbreaking quantum computing methodologies. By advancing Variational Quantum Circuit (VQC)-based approaches, the session aims to drive the development of next-generation AI-driven solutions. These solutions leverage the unique computational power of quantum systems to tackle complex challenges across diverse domains, including machine learning, artificial intelligence, multi-agent systems, signal processing, natural language processing, and a wide range of industrial and scientific applications.
Topics
This special session welcomes submissions across a diverse range of QML topics, including foundational training algorithms, trustworthy and privacy-preserving QML techniques, multi-agent learning frameworks, generative AI enhanced by quantum methodologies, and a wide array of application scenarios. Submissions may address challenges and innovations in scientific discovery as well as commercial and industrial applications, emphasizing the transformative potential of QML in real-world contexts.
Quantum machine learning as a computational agent and quantum reinforcement learning (QRL)
Quantum machine learning as generative models
Quantum machine learning in the context of trustworthy ML (e.g. differential privacy, federated learning, split learning)
Quantum machine learning with an emphasis on cybersecurity
Quantum machine learning in speech and natural language processing
Quantum machine learning for scientific discovery
Quantum machine learning for commercial and industrial applications
Quantum machine learning systems
Important Dates
Paper submission deadline: January 15, 2025
Paper acceptance notifications: Mar 31, 2025
Camera ready version due: May 1, 2025 (Hard Deadline)
Organizing Team
Organizing Committee
Samuel Yen-Chi Chen, Wells Fargo Bank, USA
Joongheon Kim, Korea University, Korea
Fan Chen, Indiana University Bloomington, USA
Prayag Tiwari, Halmstad University, Sweden
Shaukat Ali, Simula Research Laboratory, Norway
Huan-Hsin Tseng, Brookhaven National Laboratory, USA
Shinjae Yoo, Brookhaven National Laboratory, USA
Further Information
Submission Instruction: that manuscripts related to the Special Session will be submitted through the CMT paper submission website as a regular paper (Main Track) by selecting this special session “Quantum Machine Learning Algorithms and Applications” as primary Subject Area. All submitted papers will be reviewed in the same process as the regular papers. Accepted contributions will be part of the conference proceedings.