Toward Trainability of Quantum Neural Networks

Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, Dacheng Tao

Keywords: Near-term Quantum Algorithm, Quantum Neural Network, Trainability, Hierarchical Structure

Abstract: 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 the first viable solution with theoretical guarantees. Specifically, we prove that QNNs with tree tensor and step controlled architectures have gradients that vanish at most polynomially with the qubit number. Moreover, our result holds irrespective of which encoding methods are employed. We numerically demonstrate QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.

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One-sentence Summary: We analyze the trainability of quantum neural networks with special structures with proven bounds and numerical simulations.

Reviewed Version (pdf): https://openreview.net/references/pdf?id=54dVwdWkZ9