Variational Quantum Algorithm

Variational Quantum Algorithms (VQAs) are hybrid classical-quantum methods aiming to solve optimization problems on near-term quantum computers by iteratively adjusting parameters of a parameterized quantum circuit (ansatz) to minimize a cost function. Current research focuses on improving ansatz design through reinforcement learning, mitigating the "barren plateau" problem hindering optimization, and developing efficient gradient estimation techniques like the generalized Hadamard test and quantum natural gradient descent. VQAs show promise for applications in quantum machine learning, quantum chemistry, and optimization, with ongoing efforts to enhance their efficiency, accuracy, and scalability on noisy quantum hardware.