Nature Machine Intelligence最新文献_第10页

Transforming machines capable of continuous 3D shape morphing and locking 能够连续三维变形和锁定的变形机器

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-18 DOI: 10.1038/s42256-025-01028-4

Shiwei Xu, Xiaonan Hu, Ruoxi Yang, Chuanqi Zang, Lei Li, Yue Xiao, Wenbo Liu, Bocheng Tian, Wenbo Pang, Renheng Bo, Qing Liu, Youzhou Yang, Yuchen Lai, Jun Wu, Huichan Zhao, Li Wen, Yihui Zhang

{"title":"Transforming machines capable of continuous 3D shape morphing and locking","authors":"Shiwei Xu, Xiaonan Hu, Ruoxi Yang, Chuanqi Zang, Lei Li, Yue Xiao, Wenbo Liu, Bocheng Tian, Wenbo Pang, Renheng Bo, Qing Liu, Youzhou Yang, Yuchen Lai, Jun Wu, Huichan Zhao, Li Wen, Yihui Zhang","doi":"10.1038/s42256-025-01028-4","DOIUrl":"10.1038/s42256-025-01028-4","url":null,"abstract":"Inspired by natural species that leverage morphological changes to realize multiple locomotion modes, diverse multimodal robots have been reported. While developments of small-scale actuators with continuous shape morphing and locking capabilities controlled by the same energy source are crucial for miniaturization of untethered multimodal robots, it remains elusive. We introduce a synergistic design concept of small-scale continuously morphable actuators (CMAs) that harness precisely programmable actuation deformation of liquid crystal elastomer to achieve continuous shape morphing and high stiffness variation of shape memory polymer to lock geometric configuration, both through electrothermal control. Lego-inspired design strategy allows customized construction of complexly shaped CMAs (for example, ‘transformer’, ‘aircraft’ and ‘turtle’) through rational assembly of elementary actuator units with different ranges of accessible geometric configurations. The powerful shape morphing and locking capabilities, as well as the relatively high load-bearing capacity of the CMAs, allow for developments of versatile exoskeletons that can integrate a diversity of functional components. Demonstrations of unique small-scale transforming machines, such as morphable displays with a rich diversity of three-dimensional geometries, a wheeled microrobot capable of transformation among ‘sports car’, ‘winged car’ and ‘van’, and a lightweight untethered terrestrial–aerial microrobot, suggest a broad spectrum of applications. Continuous shape morphing for small robots can offer advantages, but it is difficult to perform tasks if they are not stiff enough. Xu et al. present here a design combining liquid crystal elastomers and shape memory polymers to lock morphable elements in place.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 5","pages":"703-715"},"PeriodicalIF":23.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI safety for everyone 人人享有人工智能安全

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-17 DOI: 10.1038/s42256-025-01020-y

Bálint Gyevnár, Atoosa Kasirzadeh

{"title":"AI safety for everyone","authors":"Bálint Gyevnár, Atoosa Kasirzadeh","doi":"10.1038/s42256-025-01020-y","DOIUrl":"10.1038/s42256-025-01020-y","url":null,"abstract":"Recent discussions and research in artificial intelligence (AI) safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work on AI safety necessarily entails serious consideration of potential existential threats. However, this framing has three potential drawbacks: it may exclude researchers and practitioners who are committed to AI safety but approach the field from different angles; it could lead the public to mistakenly view AI safety as focused solely on existential scenarios rather than addressing a wide spectrum of safety challenges; and it risks creating resistance to safety measures among those who disagree with predictions of existential AI risks. Here, through a systematic literature review of primarily peer-reviewed research, we find a vast array of concrete safety work that addresses immediate and practical concerns with current AI systems. This includes crucial areas such as adversarial robustness and interpretability, highlighting how AI safety research naturally extends existing technological and systems safety concerns and practices. Our findings suggest the need for an epistemically inclusive and pluralistic conception of AI safety that can accommodate the full range of safety considerations, motivations and perspectives that currently shape the field. A systematic review of peer-reviewed AI safety research reveals extensive work on practical and immediate concerns. The findings advocate for an inclusive approach to AI safety that embraces diverse motivations and perspectives.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"531-542"},"PeriodicalIF":23.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-centred design and fabrication of a wearable multimodal visual assistance system 以人为本设计和制造可穿戴多模态视觉辅助系统

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-14 DOI: 10.1038/s42256-025-01018-6

Jian Tang, Yi Zhu, Gai Jiang, Lin Xiao, Wei Ren, Yu Zhou, Qinying Gu, Biao Yan, Jiayi Zhang, Hengchang Bi, Xing Wu, Zhiyong Fan, Leilei Gu

{"title":"Human-centred design and fabrication of a wearable multimodal visual assistance system","authors":"Jian Tang, Yi Zhu, Gai Jiang, Lin Xiao, Wei Ren, Yu Zhou, Qinying Gu, Biao Yan, Jiayi Zhang, Hengchang Bi, Xing Wu, Zhiyong Fan, Leilei Gu","doi":"10.1038/s42256-025-01018-6","DOIUrl":"10.1038/s42256-025-01018-6","url":null,"abstract":"Artificial intelligence-powered wearable electronic systems offer promising solutions for non-invasive visual assistance. However, state-of-the-art systems have not sufficiently considered human adaptation, resulting in a low adoption rate among blind people. Here we present a human-centred, multimodal wearable system that advances usability by blending software and hardware innovations. For software, we customize the artificial intelligence algorithm to match the requirements of application scenario and human behaviours. For hardware, we improve the wearability by developing stretchable sensory-motor artificial skins to complement the audio feedback and visual tasks. Self-powered triboelectric smart insoles align real users with virtual avatars, supporting effective training in carefully designed scenarios. The harmonious corporation of visual, audio and haptic senses enables significant improvements in navigation and postnavigation tasks, which are experimentally evidenced by humanoid robots and participants with visual impairment in both virtual and real environments. Postexperiment surveys highlight the system’s reliable functionality and high usability. This research paves the way for user-friendly visual assistance systems, offering alternative avenues to enhance the quality of life for people with visual impairment. The development of artificial vision for blind people has been a long-standing endeavour. Tang et al. create a wearable multimodal visual assistance system with a human-centred design, blending software and hardware innovations to enhance usability.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"627-638"},"PeriodicalIF":23.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A predictive machine learning force-field framework for liquid electrolyte development 液体电解质开发的预测机器学习力场框架

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-01 DOI: 10.1038/s42256-025-01009-7

Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Xu Han, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Zhenze Yang, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang

{"title":"A predictive machine learning force-field framework for liquid electrolyte development","authors":"Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Xu Han, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Zhenze Yang, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang","doi":"10.1038/s42256-025-01009-7","DOIUrl":"10.1038/s42256-025-01009-7","url":null,"abstract":"Despite the widespread applications of machine learning force fields (MLFFs) in solids and small molecules, there is a notable gap in applying MLFFs to simulate liquid electrolytes—a critical component of current commercial lithium-ion batteries. Here we introduce ByteDance Artificial intelligence Molecular simulation Booster (BAMBOO), a predictive framework for molecular dynamics simulations, with a demonstration of its capability in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from molecular dynamics simulations. Finally, we propose a density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity and ionic conductivity across various solvents and salt combinations. The current model, trained on more than 15 chemical species, achieves an average density error of 0.01 g cm−3 on various compositions compared with experiment. A machine learning force-field framework is proposed to predict the density, viscosity and ionic conductivity of liquid electrolytes with accuracy that is higher than classical force fields.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"543-552"},"PeriodicalIF":23.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments InstaNovo能够在大规模蛋白质组学实验中进行扩散驱动的从头肽测序

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-31 DOI: 10.1038/s42256-025-01019-5

Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera-de-Torre, Jakob Berg Jespersen, Wesley Williams, Sam P. B. van Beljouw, Marcin J. Skwark, Andreas Hougaard Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin M. Schoof, Jeroen Van Goey, Ulrich auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy P. Jenkins

{"title":"InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments","authors":"Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera-de-Torre, Jakob Berg Jespersen, Wesley Williams, Sam P. B. van Beljouw, Marcin J. Skwark, Andreas Hougaard Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin M. Schoof, Jeroen Van Goey, Ulrich auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy P. Jenkins","doi":"10.1038/s42256-025-01019-5","DOIUrl":"10.1038/s42256-025-01019-5","url":null,"abstract":"Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome. InstaNovo, a transformer-based model, and InstaNovo+, a multinomial diffusion model, enhance de novo peptide sequencing, enabling discovery of novel peptides, improved therapeutics sequencing coverage and detection of unreported organisms in proteomics studies","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"565-579"},"PeriodicalIF":23.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-025-01019-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A text-guided protein design framework 一个文本引导的蛋白质设计框架

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-27 DOI: 10.1038/s42256-025-01011-z

Shengchao Liu, Yanjing Li, Zhuoxinran Li, Anthony Gitter, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Arvind Ramanathan, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar

{"title":"A text-guided protein design framework","authors":"Shengchao Liu, Yanjing Li, Zhuoxinran Li, Anthony Gitter, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Arvind Ramanathan, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar","doi":"10.1038/s42256-025-01011-z","DOIUrl":"10.1038/s42256-025-01011-z","url":null,"abstract":"Current AI-assisted protein design utilizes mainly protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in text format describing proteins’ high-level functionalities, yet whether the incorporation of such text data can help in protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multimodal framework that leverages textual descriptions for protein design. ProteinDT consists of three consecutive steps: ProteinCLAP, which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441,000 text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks. Shengchao Liu et al. present ProteinDT, a deep learning approach that can incorporate domain knowledge from textual descriptions into protein representation on a large scale.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"580-591"},"PeriodicalIF":23.9,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics 心脏和调控基因组学中变异致病性的疾病特异性语言模型

IF 23.9 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-24 DOI: 10.1038/s42256-025-01016-8

Huixin Zhan, Jason H. Moore, Zijun Zhang

{"title":"A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics","authors":"Huixin Zhan, Jason H. Moore, Zijun Zhang","doi":"10.1038/s42256-025-01016-8","DOIUrl":"10.1038/s42256-025-01016-8","url":null,"abstract":"Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in genetics. Current genomic foundation models have enhanced variant effect prediction (VEP) accuracy through weakly supervised or unsupervised training, yet these models lack disease specificity. Here, to address this, we propose DYNA (disease-specificity fine-tuning via a Siamese neural network), broadly applicable to all genomic foundation models for more effective VEPs in disease contexts. We applied DYNA to the coding VEP in cardiovascular diseases and the non-coding VEP of RNA splicing regulation. These two tasks cover a wide range of specific disease–gene relationships and disease-causing regulatory mechanisms; therefore, their performance will inform the general utility of DYNA. In both cases, DYNA fine-tunes various pretrained genomic foundation models on small rare-variant sets. The DYNA fine-tuned models show superior performance in held-out rare-variant test sets and are further replicated in large, clinically relevant variant annotations in ClinVar. Importantly, we observed that different genomic foundation models excel at different downstream VEP tasks, necessitating a universal tool such as DYNA to fully harness the power of genomic foundation models. Thus, DYNA offers a potent disease-specific VEP method for clinical variant interpretation. DYNA fine-tunes genomic foundation models with disease specificity using a Siamese network. It generalizes to rare-variant test sets and replicates results in ClinVar, advancing variant effect prediction for cardiovascular diseases and RNA splicing.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 4","pages":"661-671"},"PeriodicalIF":23.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transparency (in training data) is what we want （训练数据的）透明度是我们想要的

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-24 DOI: 10.1038/s42256-025-01023-9

引用次数: 0

Materiality and risk in the age of pervasive AI sensors 无处不在的人工智能传感器时代的物质性和风险

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-20 DOI: 10.1038/s42256-025-01017-7

Mona Sloane, Emanuel Moss, Susan Kennedy, Matthew Stewart, Pete Warden, Brian Plancher, Vijay Janapa Reddi

{"title":"Materiality and risk in the age of pervasive AI sensors","authors":"Mona Sloane, Emanuel Moss, Susan Kennedy, Matthew Stewart, Pete Warden, Brian Plancher, Vijay Janapa Reddi","doi":"10.1038/s42256-025-01017-7","DOIUrl":"10.1038/s42256-025-01017-7","url":null,"abstract":"Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the environment, autonomy and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. Here we highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models. We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act, and discuss its implementation. We conclude by advocating for increased attention to the materiality of algorithmic systems, and of on-device AI sensors in particular, and highlight the need for development of a sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency. Sloane and colleagues review emerging new dimensions of risks associated with materiality and AI algorithms run on pervasive sensors.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 3","pages":"334-345"},"PeriodicalIF":18.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum circuit optimization with AlphaTensor 基于alphatsensor的量子电路优化

IF 18.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-20 DOI: 10.1038/s42256-025-01001-1

Francisco J. R. Ruiz, Tuomas Laakkonen, Johannes Bausch, Matej Balog, Mohammadamin Barekatain, Francisco J. H. Heras, Alexander Novikov, Nathan Fitzpatrick, Bernardino Romera-Paredes, John van de Wetering, Alhussein Fawzi, Konstantinos Meichanetzidis, Pushmeet Kohli

{"title":"Quantum circuit optimization with AlphaTensor","authors":"Francisco J. R. Ruiz, Tuomas Laakkonen, Johannes Bausch, Matej Balog, Mohammadamin Barekatain, Francisco J. H. Heras, Alexander Novikov, Nathan Fitzpatrick, Bernardino Romera-Paredes, John van de Wetering, Alhussein Fawzi, Konstantinos Meichanetzidis, Pushmeet Kohli","doi":"10.1038/s42256-025-01001-1","DOIUrl":"10.1038/s42256-025-01001-1","url":null,"abstract":"A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gates), we address the problem of T-count optimization, that is, minimizing the number of T gates needed to implement a given circuit. To achieve this, we develop AlphaTensor-Quantum, a method based on deep reinforcement learning that exploits the relationship between optimizing the T-count and tensor decomposition. Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets, which substantially reduces the T-count of the optimized circuits. AlphaTensor-Quantum outperforms the existing methods for T-count optimization on a set of arithmetic benchmarks (even when compared without using gadgets). Remarkably, it discovers an efficient algorithm akin to Karatsuba’s method for multiplication in finite fields. AlphaTensor-Quantum also finds the best human-designed solutions for relevant arithmetic computations used in Shor’s algorithm and for quantum chemistry simulation, thus demonstrating that it can save hundreds of hours of research by optimizing relevant quantum circuits in a fully automated way. Ruiz and colleagues introduce AlphaTensor-Quantum, a deep reinforcement learning method for optimizing quantum circuits. It outperforms existing methods and is capable of finding the best human-designed solutions for relevant quantum computations in a fully automated way.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 3","pages":"374-385"},"PeriodicalIF":18.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-025-01001-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0