Toward Trainability of Quantum Neural Networks

Understand

Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up.

• Despite the potential to outperform classical models, serious bottlenecks exist for training QNNs; namely, QNNs with random structures have poor trainability due to the vanishing gradient with rate exponential to the input qubit number.
• The vanishing gradient could seriously influence the applications of large-size QNNs.
• In this work, we provide a viable solution with theoretical guarantees.