{"title":"jVMC:多功能和性能变分蒙特卡罗利用自动微分和GPU加速","authors":"M. Schmitt, M. Reh","doi":"10.21468/SciPostPhysCodeb.2","DOIUrl":null,"url":null,"abstract":"The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration\",\"authors\":\"M. Schmitt, M. Reh\",\"doi\":\"10.21468/SciPostPhysCodeb.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.\",\"PeriodicalId\":430271,\"journal\":{\"name\":\"SciPost Physics Codebases\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SciPost Physics Codebases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21468/SciPostPhysCodeb.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SciPost Physics Codebases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21468/SciPostPhysCodeb.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.