Quantum Neural Networks (QNNs): How They Are Built
How quantum neural networks are built from parameterized quantum circuits, how they are trained and where they might be useful.
How quantum neural networks are built from parameterized quantum circuits, how they are trained and where they might be useful.
How electrons move through nanowires, quantum dots and other nanostructures, and how confinement and tunneling shape device behavior.
A wave packet is a localized quantum state that spreads over time. Its evolution under the Schrödinger equation, tunneling, revivals and experiments.
How information flows causally through quantum systems, the frameworks and graphs used to describe it, and the challenges posed by entanglement.
Max-entropy is the partner of min-entropy. Its definition and operational meaning in data compression and quantum information processing.
Min-entropy measures the maximum probability of guessing a value. How its quantum version sets how much randomness can be extracted.
Distinguishability measures for quantum states compared: trace distance, fidelity, relative entropy and others, with their uses.
How quantum information moves through a circuit: encoding, gates, entanglement, measurement and the limits information theory places on it.
Private channels transmit quantum information with perfect secrecy against an eavesdropper. Private capacity, QKD and practical limits.
Quantum causal models extend causal graphs to quantum systems and help explain correlations. The framework and what it clarifies.
Quantum causal networks describe how cause and effect work between quantum systems. Their structure, properties and relation to non-locality.
The amplitude damping channel models energy loss, such as a qubit decaying to its ground state. Its Kraus operators, effects and correction.