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Provable bounds for noise-free expectation values computed from noisy samples. 从噪声样本计算出的无噪声期望值的可证明边界。
IF 12
Nature computational science Pub Date : 2024-11-01 DOI: 10.1038/s43588-024-00709-1
Samantha V Barron, Daniel J Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner
{"title":"Provable bounds for noise-free expectation values computed from noisy samples.","authors":"Samantha V Barron, Daniel J Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner","doi":"10.1038/s43588-024-00709-1","DOIUrl":"https://doi.org/10.1038/s43588-024-00709-1","url":null,"abstract":"<p><p>Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today's quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
E-waste challenges of generative artificial intelligence. 生成式人工智能面临的电子垃圾挑战。
IF 12
Nature computational science Pub Date : 2024-10-28 DOI: 10.1038/s43588-024-00712-6
Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen
{"title":"E-waste challenges of generative artificial intelligence.","authors":"Peng Wang, Ling-Yu Zhang, Asaf Tzachor, Wei-Qiang Chen","doi":"10.1038/s43588-024-00712-6","DOIUrl":"10.1038/s43588-024-00712-6","url":null,"abstract":"<p><p>Generative artificial intelligence (GAI) requires substantial computational resources for model training and inference, but the electronic-waste (e-waste) implications of GAI and its management strategies remain underexplored. Here we introduce a computational power-driven material flow analysis framework to quantify and explore ways of managing the e-waste generated by GAI, with a particular focus on large language models. Our findings indicate that this e-waste stream could increase, potentially reaching a total accumulation of 1.2-5.0 million tons during 2020-2030, under different future GAI development settings. This may be intensified in the context of geopolitical restrictions on semiconductor imports and the rapid server turnover for operational cost savings. Meanwhile, we show that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16-86%. This underscores the importance of proactive e-waste management in the face of advancing GAI technologies.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Publisher Correction: Reliable deep learning in anomalous diffusion against out-of-distribution dynamics. 出版商更正：针对分布外动态异常扩散的可靠深度学习。
IF 12
Nature computational science Pub Date : 2024-10-28 DOI: 10.1038/s43588-024-00729-x
Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji
{"title":"Publisher Correction: Reliable deep learning in anomalous diffusion against out-of-distribution dynamics.","authors":"Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji","doi":"10.1038/s43588-024-00729-x","DOIUrl":"https://doi.org/10.1038/s43588-024-00729-x","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy. 通过几何学习和预训练策略改进突变后蛋白质稳定性变化的预测。
IF 12
Nature computational science Pub Date : 2024-10-25 DOI: 10.1038/s43588-024-00716-2
Yunxin Xu, Di Liu, Haipeng Gong
{"title":"Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy.","authors":"Yunxin Xu, Di Liu, Haipeng Gong","doi":"10.1038/s43588-024-00716-2","DOIUrl":"10.1038/s43588-024-00716-2","url":null,"abstract":"<p><p>Accurate prediction of protein mutation effects is of great importance in protein engineering and design. Here we propose GeoStab-suite, a suite of three geometric learning-based models-GeoFitness, GeoDDG and GeoDTm-for the prediction of fitness score, ΔΔG and ΔT<sub>m</sub> of a protein upon mutations, respectively. GeoFitness engages a specialized loss function to allow supervised training of a unified model using the large amount of multi-labeled fitness data in the deep mutational scanning database. To further improve the downstream tasks of ΔΔG and ΔT<sub>m</sub> prediction, the encoder of GeoFitness is reutilized as a pre-trained module in GeoDDG and GeoDTm to overcome the challenge of lacking sufficient labeled data. This pre-training strategy, in combination with data expansion, markedly improves model performance and generalizability. In the benchmark test, GeoDDG and GeoDTm outperform the other state-of-the-art methods by at least 30% and 70%, respectively, in terms of the Spearman correlation coefficient.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fostering discussions on topical issues 促进对热点问题的讨论。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00719-z
{"title":"Fostering discussions on topical issues","authors":"","doi":"10.1038/s43588-024-00719-z","DOIUrl":"10.1038/s43588-024-00719-z","url":null,"abstract":"Nature Computational Science invites researchers to submit Correspondence pieces.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"723-723"},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00719-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. 作者更正：用于高效 ab initio 电子结构计算的深度学习密度泛函理论哈密顿。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00723-3
He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu
{"title":"Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation.","authors":"He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu","doi":"10.1038/s43588-024-00723-3","DOIUrl":"https://doi.org/10.1038/s43588-024-00723-3","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Taking a deep dive with active learning for drug discovery 利用主动学习深入研究药物发现。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00704-6
Zachary Fralish, Daniel Reker
{"title":"Taking a deep dive with active learning for drug discovery","authors":"Zachary Fralish,&nbsp;Daniel Reker","doi":"10.1038/s43588-024-00704-6","DOIUrl":"10.1038/s43588-024-00704-6","url":null,"abstract":"Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"727-728"},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An interdisciplinary effort to integrate coding into science courses. 将编码纳入科学课程的跨学科努力。
IF 12
Nature computational science Pub Date : 2024-10-22 DOI: 10.1038/s43588-024-00708-2
Christina L Vizcarra, Ryan F Trainor, Ashley Ringer McDonald, Chris T Richardson, Davit Potoyan, Jessica A Nash, Britt Lundgren, Tyler Luchko, Glen M Hocky, Jonathan J Foley, Timothy J Atherton, Grace Y Stokes
{"title":"An interdisciplinary effort to integrate coding into science courses.","authors":"Christina L Vizcarra, Ryan F Trainor, Ashley Ringer McDonald, Chris T Richardson, Davit Potoyan, Jessica A Nash, Britt Lundgren, Tyler Luchko, Glen M Hocky, Jonathan J Foley, Timothy J Atherton, Grace Y Stokes","doi":"10.1038/s43588-024-00708-2","DOIUrl":"https://doi.org/10.1038/s43588-024-00708-2","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The future of machine learning for small-molecule drug discovery will be driven by data 小分子药物发现机器学习的未来将由数据驱动。
IF 12
Nature computational science Pub Date : 2024-10-15 DOI: 10.1038/s43588-024-00699-0
Guy Durant, Fergus Boyles, Kristian Birchall, Charlotte M. Deane
{"title":"The future of machine learning for small-molecule drug discovery will be driven by data","authors":"Guy Durant,&nbsp;Fergus Boyles,&nbsp;Kristian Birchall,&nbsp;Charlotte M. Deane","doi":"10.1038/s43588-024-00699-0","DOIUrl":"10.1038/s43588-024-00699-0","url":null,"abstract":"Many studies have prophesied that the integration of machine learning techniques into small-molecule therapeutics development will help to deliver a true leap forward in drug discovery. However, increasingly advanced algorithms and novel architectures have not always yielded substantial improvements in results. In this Perspective, we propose that a greater focus on the data for training and benchmarking these models is more likely to drive future improvement, and explore avenues for future research and strategies to address these data challenges. The application of machine learning techniques to small-molecule drug discovery has not yet yielded a true leap forward in the field. This Perspective discusses how a renewed focus on data and validation could help unlock machine learning’s potential.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"735-743"},"PeriodicalIF":12.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The decomposition of perturbation modeling 扰动建模的分解。
IF 12
Nature computational science Pub Date : 2024-10-14 DOI: 10.1038/s43588-024-00706-4
Stefan Peidli
{"title":"The decomposition of perturbation modeling","authors":"Stefan Peidli","doi":"10.1038/s43588-024-00706-4","DOIUrl":"10.1038/s43588-024-00706-4","url":null,"abstract":"A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"725-726"},"PeriodicalIF":12.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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