I will discuss how concepts and tools from quantum information theory, in particular the notion of entanglement, can be applied to elementary particle scattering at high-energy colliders. In this framework, the discrete quantum numbers of scattered particles are identified with quantum-information-theoretic qubits, and the resulting density matrix reflects the structure of the underlying...
Recently, quantum machine learning, particularly quantum reservoir computing, has attracted attention for directly utilizing the natural complex dynamics of quantum systems for information processing. It is believed that by utilizing the vast internal space (Hilbert space) of quantum systems, performance comparable to classical models can be achieved with only a few qubits. However, encoding...
AI now rivals or surpasses humans on many well-defined tasks, sharpening the need to clarify what "information" means across disciplines. This talk frames information operationally: Shannon entropy for classical systems and von Neumann entropy for quantum states, and shows how these measures structure, analyze, and reduce uncertainty in machine learning and data-driven inference. We compare...
Medical imaging provides a demanding test for quantum machine learning because clinically relevant images are much larger and structurally richer than standard benchmarks (MNIST). In this work, we study quantum extreme reservoir computing (QERC) for binary brain-tumour classification using the Br35H MRI dataset, with images resized to 150 × 150 pixels. Since such inputs cannot be encoded...
