A Variational Approach to Quantum Gated Recurrent Units

IF 1.1 Q3 PHYSICS, MULTIDISCIPLINARY
Andrea Ceschini, A. Rosato, Massimo Panella
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引用次数: 0

Abstract

Quantum Recurrent Neural Networks are receiving an increased attention thanks to their enhanced generalization capabilities in time series analysis. However, their performances were bottlenecked by long training times and unscalable architectures. In this paper, we propose a novel Quantum Recurrent Neural Network model based on Quantum Gated Recurrent Units. It uses a learnable Variational Quantum Layer to process temporal data, interspersed with two classical layers to properly match the dimensionality of the input and output vectors. Such an architecture has fewer quantum parameters than existing Quantum Long Short-Term Memory models. Both the quantum networks were evaluated on periodic and real-world time series datasets, together with the classical counterparts. The quantum models exhibited superior performances compared to the classical ones in all the test cases. The Quantum Gated Recurrent Units outperformed the Quantum Long Short-Term Memory network despite having a simpler internal configuration. Moreover, the Quantum Gated Recurrent Units network demonstrated to be about 25% faster during the training and inference procedure over the Quantum Long Short-Term Memory. This improvement in speed comes with one less quantum circuit to be executed, suggesting that our model may offer a more efficient alternative for implementing Quantum Recurrent Neural Networks on both simulated and real quantum hardware.
量子门控循环单元的变异方法
量子递归神经网络在时间序列分析中具有更强的泛化能力,因此受到越来越多的关注。然而,由于训练时间长和架构不可扩展,量子递归神经网络的性能遇到了瓶颈。在本文中,我们提出了一种基于量子门控递归单元的新型量子递归神经网络模型。它使用可学习的变异量子层来处理时间数据,中间穿插两个经典层,以适当匹配输入和输出向量的维度。与现有的量子长短期记忆模型相比,这种架构的量子参数更少。我们在周期性和真实世界时间序列数据集上对这两种量子网络以及经典网络进行了评估。在所有测试案例中,量子模型都表现出优于经典模型的性能。尽管量子门控递归单元的内部配置更简单,但其性能却优于量子长短期记忆网络。此外,量子门控递归单元网络在训练和推理过程中的速度比量子长短时记忆网络快约 25%。这一速度的提高是在少执行一个量子电路的情况下实现的,这表明我们的模型可以为在模拟和真实量子硬件上实现量子递归神经网络提供更有效的替代方案。
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来源期刊
Journal of Physics Communications
Journal of Physics Communications PHYSICS, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
0.00%
发文量
114
审稿时长
10 weeks
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