Parameterized Quantum

Parameterized quantum circuits, employing classical optimization to adjust parameters controlling quantum states, are central to emerging quantum machine learning algorithms. Current research focuses on addressing challenges like barren plateaus (exponentially small gradients hindering training), efficient gradient estimation techniques (including leveraging shadow tomography), and the impact of noise on model trainability and performance, often using variational methods and quantum neural networks. These investigations aim to improve the efficiency and reliability of quantum algorithms for applications such as quantum simulation, metrology, and reinforcement learning, ultimately determining the viability of quantum advantage in these domains.