A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature
Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12515-4 |
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| author | Trilok Nath Pandey Vishvajeet Ravalekar Sidharth D. Nair Sunil Kumar Pradhan |
| author_facet | Trilok Nath Pandey Vishvajeet Ravalekar Sidharth D. Nair Sunil Kumar Pradhan |
| author_sort | Trilok Nath Pandey |
| collection | DOAJ |
| description | Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study’s findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making. |
| format | Article |
| id | doaj-art-163cc3e0d86d42cf8689a63138e26b5b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-163cc3e0d86d42cf8689a63138e26b5b2025-08-20T03:46:00ZengNature PortfolioScientific Reports2045-23222025-08-0115112110.1038/s41598-025-12515-4A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperatureTrilok Nath Pandey0Vishvajeet Ravalekar1Sidharth D. Nair2Sunil Kumar Pradhan3School of Computer Science and Engineering, Vellore Institute of TechnologySchool of Computer Science and Engineering, Vellore Institute of TechnologySchool of Electronics Engineering, Vellore Institute of TechnologySchool of Electronics Engineering, Vellore Institute of TechnologyAbstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study’s findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.https://doi.org/10.1038/s41598-025-12515-4Quantum neural networksNoisy intermediate scale quantumAutoregressive moving averageAutoregressiveIntegrated moving averageSeasonal autoregressive integrated moving average |
| spellingShingle | Trilok Nath Pandey Vishvajeet Ravalekar Sidharth D. Nair Sunil Kumar Pradhan A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature Scientific Reports Quantum neural networks Noisy intermediate scale quantum Autoregressive moving average Autoregressive Integrated moving average Seasonal autoregressive integrated moving average |
| title | A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature |
| title_full | A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature |
| title_fullStr | A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature |
| title_full_unstemmed | A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature |
| title_short | A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature |
| title_sort | comparative analysis of classical machine learning models with quantum inspired models for predicting world surface temperature |
| topic | Quantum neural networks Noisy intermediate scale quantum Autoregressive moving average Autoregressive Integrated moving average Seasonal autoregressive integrated moving average |
| url | https://doi.org/10.1038/s41598-025-12515-4 |
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