Trainability of Dissipative Perceptron-Based Quantum Neural Networks.
Phys Rev Lett
; 128(18): 180505, 2022 May 06.
Article
em En
| MEDLINE
| ID: mdl-35594093
ABSTRACT
Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. Here, we analyze the gradient scaling (and hence the trainability) for a recently proposed architecture that we call dissipative QNNs (DQNNs), where the input qubits of each layer are discarded at the layer's output. We find that DQNNs can exhibit barren plateaus, i.e., gradients that vanish exponentially in the number of qubits. Moreover, we provide quantitative bounds on the scaling of the gradient for DQNNs under different conditions, such as different cost functions and circuit depths, and show that trainability is not always guaranteed. Our work represents the first rigorous analysis of the scalability of a perceptron-based QNN.
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01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article