QSegRNN: quantum segment recurrent neural network for time series forecasting

IF 5.8 2区 物理与天体物理 Q1 OPTICS
Kyeong-Hwan Moon, Seon-Geun Jeong, Won-Joo Hwang
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引用次数: 0

Abstract

Recently many data centers have been constructed for artificial intelligence (AI) research. The important condition of the data center is to supply sufficient electricity, resulting in many electricity transformers being installed. Especially, these electricity transformers have led to significant heat generation in many data centers. Therefore, managing the temperature of electricity transformers has emerged as an important task. Notably, numerous studies are being conducted to manage and forecast the temperature of electricity transformers using artificial intelligence models. However, as the size of predictive models increases and computational demands grow, substantial computing resources are required. Consequently, there are instances where the lack of computing resources makes these models difficult to operate. To address these challenges, we propose a quantum segment recurrent neural network (QSegRNN), a time series forecasting model utilizing quantum computing. QSegRNN leverages quantum computing to achieve comparable performance with fewer parameters than classical counterpart models under similar conditions. QSegRNN inspired by a classical SegRNN uses the quantum cell instead of the classical cell in the model. The advantage of this structure is that it can be designed with fewer parameters under similar architecture. To construct the quantum cell, we benchmark the quantum convolutional circuit with amplitude embedding as the variational quantum circuit, minimizing information loss while considering the limit of noisy intermediate-scale quantum (NISQ) devices. The experiment result illustrates that the forecasting performance of QSegRNN achieves better performance than SegRNN and other forecasting models even though QSegRNN has only 85 percent of the parameters.

QSegRNN:时间序列预测的量子段递归神经网络
近年来,人们建立了许多用于人工智能研究的数据中心。数据中心的重要条件是供电充足,因此需要安装很多变压器。特别是,这些电力变压器在许多数据中心产生了大量的热量。因此,变压器的温度管理已成为一项重要的任务。值得注意的是,目前正在进行大量研究,利用人工智能模型来管理和预测变压器的温度。然而,随着预测模型规模的增加和计算需求的增长,需要大量的计算资源。因此,在某些情况下,由于缺乏计算资源,这些模型难以操作。为了解决这些挑战,我们提出了一种量子段递归神经网络(QSegRNN),一种利用量子计算的时间序列预测模型。QSegRNN利用量子计算在类似条件下以更少的参数实现与经典对应模型相当的性能。受经典SegRNN启发的QSegRNN在模型中使用量子细胞代替经典细胞。这种结构的优点是可以在相似的结构下用更少的参数进行设计。为了构建量子单元,我们将振幅嵌入的量子卷积电路作为变分量子电路的基准,在考虑噪声中尺度量子(NISQ)器件限制的同时,最大限度地减少了信息损失。实验结果表明,QSegRNN的预测性能优于SegRNN和其他预测模型,即使QSegRNN只有85%的参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Quantum Technology
EPJ Quantum Technology Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
7.70
自引率
7.50%
发文量
28
审稿时长
71 days
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. EPJ Quantum Technology covers theoretical and experimental advances in subjects including but not limited to the following: Quantum measurement, metrology and lithography Quantum complex systems, networks and cellular automata Quantum electromechanical systems Quantum optomechanical systems Quantum machines, engineering and nanorobotics Quantum control theory Quantum information, communication and computation Quantum thermodynamics Quantum metamaterials The effect of Casimir forces on micro- and nano-electromechanical systems Quantum biology Quantum sensing Hybrid quantum systems Quantum simulations.
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