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.
