Lajos Hanzo

IEEE International Conference on Acoustics, Speech and Signal Processing; Barcelona, Spain; May 4-8, 2026

This short course, delivered by Lajos Hanzo, offers a comprehensive examination of quantum information processing, sensing, and communications, bridging the gap between theoretical principles and practical signal processing implementations. Motivated by the physical limitations of classical scaling and Moore's Law, the tutorial outlines the foundational mechanics of quantum systems—including superposition, entanglement, measurement, and decoherence—and evaluates their realistic potential for next-generation wireless networks.

The core of the presentation explores the integration of quantum algorithms into key signal processing domain challenges, such as multi-user detection, channel estimation, array localization, and network routing. It emphasizes quantum error correction (QEC) and mitigation strategies by drawing direct parallels to classical coding theory, illustrating how stabilizer codes and parity-check formalisms adapt to quantum channels. Furthermore, the discussion surveys technical milestones in quantum key distribution (QKD), satellite-assisted quantum communications, and distributed quantum network architectures.

Ultimately, the course demystifies widespread misconceptions surrounding quantum capabilities while establishing a clear engineering roadmap for future communications. By identifying critical knowledge gaps and open research directions, the talk underscores how leveraging classical information-theoretic principles will accelerate the realization of scalable, fault-tolerant, and inherently secure quantum networks.

This talk explores the convergence of classical signal processing and quantum-domain information systems, driven by the limitations of classical semiconductor scaling. As integrated circuits approach nanoscale dimensions, quantum effects become unavoidable, requiring a shift toward quantum-native information processing models. The primary goal is to establish a unified framework that bridges classical signal processing concepts—such as error correction, filtering, and machine learning—with quantum computing, quantum communications, and quantum sensing to unlock capabilities beyond classical limits.

Key focus areas include the physical constraints of current quantum hardware, particularly limited qubit counts and high vulnerability to quantum decoherence, which manifests as bit-flips and phase-flips. To address these challenges, sophisticated quantum error mitigation and error correction strategies derived from classical coding theory are essential for reliable operation. Furthermore, the discussion highlights recent progress in secure quantum communication protocols, quantum machine learning architectures, and the potential realization of Integrated Sensing and Communication (ISAC) operating within the quantum domain.

Ultimately, entangling classical signal processing techniques with quantum-domain physics provides a practical roadmap for overcoming current hardware limitations. While large-scale fault-tolerant quantum systems remain a long-term goal, near-term hybrid classical-quantum paradigms offer immediate opportunities to enhance secure communications, high-dimensional data processing, and ultra-precise sensing.