arXiv 2202.012552022-02physics.comp-phphysics.chem-ph

Large scale quantum chemistry with Tensor Processing Units

Ryan Pederson, John Kozlowski, Ruyi Song, Jackson Beall, Martin Ganahl, Markus Hauru, Adam G. M. Lewis, Yi Yao, Shrestha Basu Mallick, Volker Blum, Guifre Vidal

DOI：: 10.1021/acs.jctc.2c00876

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

We demonstrate the use of Google's cloud-based Tensor Processing Units (TPUs) to accelerate and scale up conventional (cubic-scaling) density functional theory (DFT) calculations. Utilizing 512 TPU cores, we accomplish the largest such DFT computation to date, with 247848 orbitals, corresponding to a cluster of 10327 water molecules with 103270 electrons, all treated explicitly. Our work thus paves the way towards accessible and systematic use of conventional DFT, free of any system-specific constraints, at unprecedented scales.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

量子化学中的随机张量收缩

Stochastic tensor contraction for quantum chemistry

Division of Chemistry and Chemical Engineering, California Institute of Technology（加州理工学院化学与化学工程系）；Marcus Center for Theoretical Chemistry, California Institute of Technology（加州理工学院马库斯理论化学中心）

本文提出随机张量收缩方法，用于降低高阶张量收缩的计算成本，应用于耦合簇理论，实现更精确的计算并挑战现有成本与精度关系。

2026-05-07cond-mat.str-elphysics.chem-phphysics.comp-ph

通过张量分解解决棘手的化学问题

Solving intractable chemical problems by tensor decomposition

本文探讨张量分解技术如何克服传统数值方法在处理大分子时的计算瓶颈，通过将大型数值表示分解为更小可处理的表示，成为计算化学各领域的最先进方法，并基于张量网络形式提供统一视角。

2024-01-29physics.chem-phphysics.comp-phphysics.data-an

TAMM：面向多体方法的张量代数框架

TAMM: Tensor Algebra for Many-body Methods

本文提出TAMM框架，通过解耦计算规格与执行，兼顾领域科学家算法开发和高性能计算优化，实现计算化学张量收缩方法的高效、可移植开发。

2023-07-10cs.DCphysics.chem-phphysics.comp-ph

Automatic transformation of irreducible representations for efficient contraction of tensors with cyclic group symmetry

Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-…

2022-09-25physics.comp-ph

CheMPS2: a free open-source spin-adapted implementation of the density matrix renormalization group for ab initio quantum chemistry

The density matrix renormalization group (DMRG) has become an indispensable numerical tool to find exact eigenstates of finite-size quantum systems with strong correlation. In the fields of condensed matter, nuclear structure and molecular electronic structure, it has significantly extended the system sizes that can be handled compared to full configuration interaction, without losing numerical accuracy. For quantum chemistry (QC), the most efficient implementations of DMRG require the incorporation of particle number, spin and point group symmetries in the underlying matrix product state (MPS…

2014-01-08cond-mat.str-elphysics.chem-phphysics.comp-ph

在量子计算机上实现尺寸一致的量子化学

Size-Consistent Quantum Chemistry on Quantum Computers

The University of Chicago（芝加哥大学）

本文通过模拟由增多非相互作用H₂分子组成的系统，评估了量子硬件上的尺寸一致性，发现当前量子设备在化学相关系统大小上能保持尺寸一致性，支持了强相关分子和材料的可扩展模拟可行性。

2025-12-20physics.chem-phphysics.comp-phquant-ph

更多相关论文收起更多论文

Analysis of The Tailored Coupled-Cluster Method in Quantum Chemistry

In quantum chemistry, one of the most important challenges is the static correlation problem when solving the electronic Schrödinger equation for molecules in the Born--Oppenheimer approximation. In this article, we analyze the tailored coupled-cluster method (TCC), one particular and promising method for treating molecular electronic-structure problems with static correlation. The TCC method combines the single-reference coupled-cluster (CC) approach with an approximate reference calculation in a subspace [complete active space (CAS)] of the considered Hilbert space that covers the static cor…

2019-10-13cond-mat.str-elcs.NAmath.NA

Discontinuous Galerkin method with Voronoi partitioning for Quantum Simulation of Chemistry

Molecular orbitals based on the linear combination of Gaussian type orbitals are arguably the most employed discretization in quantum chemistry simulations, both on quantum and classical devices. To circumvent a potentially dense two-body interaction tensor and obtain lower asymptotic costs for quantum simulations of chemistry, the discontinuous Galerkin (DG) procedure using a rectangular partitioning strategy was recently piloted [McClean et al, New J. Phys. 22, 093015, 2020]. The DG approach interpolates in a controllable way between a compact description of the two-body interaction tensor t…

2020-10-31physics.comp-phquant-ph

Resource Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach

Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and to enable more realistic si…

2020-04-16physics.comp-phquant-ph

Efficient step-merged quantum imaginary time evolution algorithm for quantum chemistry

We develop a resource efficient step-merged quantum imaginary time evolution approach (smQITE) to solve for the ground state of a Hamiltonian on quantum computers. This heuristic method features a fixed shallow quantum circuit depth along the state evolution path. We use this algorithm to determine binding energy curves of a set of molecules, including H$_2$, H$_4$, H$_6$, LiH, HF, H$_2$O and BeH$_2$, and find highly accurate results. The required quantum resources of smQITE calculations can be further reduced by adopting the circuit form of the variational quantum eigensolver (VQE) technique,…

2020-09-02cond-mat.str-elphysics.chem-phphysics.comp-ph

Faster identification of optimal contraction sequences for tensor networks

The efficient evaluation of tensor expressions involving sums over multiple indices is of significant importance to many fields of research, including quantum many-body physics, loop quantum gravity, and quantum chemistry. The computational cost of evaluating an expression may depend strongly upon the order in which the index sums are evaluated, and determination of the operation-minimising contraction sequence for a single tensor network (single term, in quantum chemistry) is known to be NP-hard. The current preferred solution is an exhaustive search, using either an iterative depth-first app…

2015-12-23cond-mat.str-elphysics.comp-phquant-ph

Assembling Cartesian Harmonic Tensors

Cartesian harmonic tensors are completely symmetric traceless tensors in three dimensional space constructed from the direct product of unit vectors. They are useful in generating expressions for the angular coupling of spherical harmonics of differing solid angles in terms of scalar and vector products of the unit vectors for the spherical harmonics involved. A presentation for the algebraic programming of Cartesian harmonic tensors of arbitrary rank is given using Algebraic Assembly Language. Results are checked using Mathematica.

2023-02-25physics.class-phphysics.comp-ph

A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units

A computational fluid dynamics (CFD) simulation framework for fluid-flow prediction is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated dense matrix multiplication, large high bandwidth memory, and a fast inter-chip interconnect, making it attractive for high-performance scientific computing. The CFD framework solves the variable-density Navier-Stokes equation using a low-Mach approximation, and the governing equations are discretized by a finite-difference method on a collocated structured mesh. It uses the graph-based TensorFlow as the…

2022-01-18cs.NAmath.NAphysics.comp-ph

Large Scale Distributed Linear Algebra With Tensor Processing Units

We have repurposed Google Tensor Processing Units (TPUs), application-specific chips developed for machine learning, into large-scale dense linear algebra supercomputers. The TPUs' fast inter-core interconnects (ICI)s, physically two-dimensional network topology, and high-bandwidth memory (HBM) permit distributed matrix multiplication algorithms to rapidly become computationally bound. In this regime, the matrix-multiply units (MXU)s dominate the runtime, yielding impressive scaling, performance, and raw size: operating in float32 precision, a full 2048-core pod of third generation TPUs can mu…

2021-12-16cs.DCphysics.comp-phquant-ph