Variational Quantum Circuit

Variational quantum circuits (VQCs) are hybrid quantum-classical algorithms aiming to leverage quantum computers for machine learning and optimization tasks by optimizing a parameterized quantum circuit to minimize a cost function. Current research focuses on improving VQC performance through enhanced optimization techniques (e.g., quantum natural gradient, metaheuristics, and adaptive pruning), efficient circuit design (including architecture search and data embedding strategies), and mitigating challenges like barren plateaus. These advancements are crucial for realizing the potential of near-term quantum computers in diverse applications, ranging from anomaly detection and reinforcement learning to solving complex scientific problems.