Parameterized Quantum Circuit

Parameterized quantum circuits (PQCs) are programmable quantum circuits whose parameters are optimized classically to perform specific tasks, primarily in quantum machine learning and optimization. Current research focuses on improving PQC design through techniques like reinforcement learning for automated architecture search, enhancing training efficiency via methods such as quantum natural gradient descent and mitigating noise effects, and understanding their expressiveness and limitations. This field is crucial for advancing variational quantum algorithms and exploring the potential of near-term quantum computers for practical applications, particularly in areas where classical methods struggle with scalability or noise.