{"title":"QSegRNN: quantum segment recurrent neural network for time series forecasting","authors":"Kyeong-Hwan Moon, Seon-Geun Jeong, Won-Joo Hwang","doi":"10.1140/epjqt/s40507-025-00333-6","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00333-6","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPJ Quantum Technology","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1140/epjqt/s40507-025-00333-6","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
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.
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.