Quantum computing has seen remarkable progress in recent years, advancing from its theoretical foundations in the 1980s to the development of hardware prototypes in the 2000s capable of supporting hundreds of qubits. Although still in its nascent stage, the rapid advancements in quantum hardware have sparked speculation that Noisy Intermediate-Scale Quantum (NISQ) devices may soon surpass classical computers in performance. Among the various algorithms tailored for NISQ devices, Variational Quantum Algorithms (VQAs) have emerged as particularly promising, owing to their ability to function with a limited number of qubits while demonstrating resilience to noise. VQAs are typically classified as hybrid quantum-classical approaches, which facilitate the practical application of hybrid quantum-classical machine learning algorithms by integrating classical machine learning and signal processing models. These approaches provide a robust framework that harnesses the strengths of both quantum and classical computing.
In this workshop, we propose a comprehensive exploration of VQA-based quantum algorithms, with the objective of advancing the state-of-the-art in quantum machine learning algorithms. We will delve into their applications across machine learning, artificial intelligence, and signal processing challenges, aiming to push the boundaries of what is achievable with quantum computing in these domains.
We solicit contributions spanning a comprehensive spectrum of QML research from foundational training algorithms to applications in privacy and security, intended to include scientific, commercial, and industrial domains. By offering a platform for cutting-edge research, this workshop aims to catalyze the adoption and innovation of QML technologies, bridging the gap between quantum advancements and traditional signal processing challenges.
Quantum machine learning in the context of trustworthy ML (e.g. differential privacy, federated 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
Workshop Paper Submission Deadline: Novemebr 8, 2024
Workshop Paper Acceptance Notification: December 18, 2024
Workshop Camera Ready Paper Deadline: January 13, 2025
ICASSP 2025 official dates: Important Dates – 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ieeeicassp.org)
ICASSP 2025 submission guideline: Publishing and Paper Presentation Options – 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ieeeicassp.org)