The rapid evolution of quantum computing, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, has opened new opportunities at the intersection of quantum information science, machine learning, and signal processing. Among the various algorithmic paradigms, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for leveraging near-term quantum devices, thanks to their hybrid quantum-classical structure and partial noise resilience.
This workshop will explore the frontier of VQA-based quantum machine learning (QML) models, with an emphasis on signal processing-inspired architectures and learning tasks. In particular, we will highlight emerging applications in biomedical signal processing, including quantum-enhanced models for biosignal classification, time-series forecasting, and neural signal decoding.
By bringing together researchers from quantum computing, signal processing, artificial intelligence, and physics, the workshop aims to foster interdisciplinary exchange, showcase recent advances in quantum-enhanced learning algorithms, and assess their potential impact on real-world applications. The program will feature invited talks, contributed papers, and panel discussions that highlight the synergies between quantum algorithms and signal processing techniques.
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, adversarial robustness)
Fairness in quantum machine 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 biomedical signal processing
Quantum machine learning integration with communication protocols
Quantum machine learning for commercial and industrial applications
Quantum machine learning systems
Quantum machine learning with multi-agent framework
Quantum machine learning algorithms for fault-tolerant quantum computers (FTQC)
Scalability of quantum machine learning
Workshop Paper Submission Deadline: October 22, 2025
Workshop Paper Acceptance Notification: December 10, 2025
Workshop Camera Ready Paper Deadline: January 7, 2026
ICASSP 2026 official dates: 2026.ieeeicassp.org/eventtype/important-dates/
ICASSP 2026 submission guideline: 2026.ieeeicassp.org/paper-submission-instructions/
Submission Link: https://cmsworkshops.com/ICASSP2026/Papers/Submission.asp?Type=WS&ID=4