Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Understand

Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years.

• However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number.
• This result leads to a general standpoint that deep quantum circuits would not be feasible for practical tasks.
• In this work, we propose an initialization strategy with theoretical guarantees for the vanishing gradient problem in general deep quantum circuits.