Ensemble fluid simulations on quantum computers

被引：10

作者：

Succi, Sauro ^{[1
]}

Itani, Wael ^{[2
]}

Sanavio, Claudio ^{[1
]}

Sreenivasan, Katepalli R. ^{[2
,3
,4
]}

Steijl, Rene ^{[5
]}

机构：

[1] Fdn Ist Italiano Tecnol, Ctr Life Nanoneurosci Sapienza, Viale Regina Elena 291, I-00161 Rome, Italy

[2] NYU, Tandon Sch Engn, Brooklyn, NY 11201 USA

[3] NYU, Courant Inst Math Sci, New York, NY 10012 USA

[4] NYU, Dept Phys, New York, NY 10003 USA

[5] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Scotland

来源：

COMPUTERS & FLUIDS | 2024年 / 270卷

关键词：

Fluid dynamics; Liouville; Quantum algorithm; Kinetic theory; Lattice; Navier-Stokes;

D O I：

10.1016/j.compfluid.2023.106148

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We discuss the viability of ensemble simulations of fluid flows on quantum computers. The basic idea is to formulate a functional Liouville equation for the probability distribution of the flow field configuration and recognize that, due to its linearity, such an equation is in principle more amenable to quantum computing than the dynamic equations of fluid motion. After suitable marginalization and associated closure, the Liouville approach is shown to require several hundreds of logical qubits, hence calling for a major thrust in current noise correction and mitigation techniques.

引用

页数：7

共 31 条

[1] THE LATTICE BOLTZMANN-EQUATION - THEORY AND APPLICATIONS [J].

BENZI, R ;

SUCCI, S ;

VERGASSOLA, M .

PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 1992, 222 (03) :145-197

[2] Quantum Algorithm for Linear Differential Equations with Exponentially Improved Dependence on Precision [J].

Berry, Dominic W. ;

Childs, Andrew M. ;

Ostrander, Aaron ;

Wang, Guoming .

COMMUNICATIONS IN MATHEMATICAL PHYSICS, 2017, 356 (03) :1057-1081

[3] High-order quantum algorithm for solving linear differential equations [J].

Berry, Dominic W. .

JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2014, 47 (10)

[4]

Bharadwaj SS, 2020, Pramana Indian Acad Sci Conf Aer, V3, P77

[5] Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers [J].

Bhati, Agastya P. ;

Wan, Shunzhou ;

Alfe, Dario ;

Clyde, Austin R. ;

Bode, Mathis ;

Tan, Li ;

Titov, Mikhail ;

Merzky, Andre ;

Turilli, Matteo ;

Jha, Shantenu ;

Highfield, Roger R. ;

Rocchia, Walter ;

Scafuri, Nicola ;

Succi, Sauro ;

Kranzlmueller, Dieter ;

Mathias, Gerald ;

Wifling, David ;

Donon, Yann ;

Di Meglio, Alberto ;

Vallecorsa, Sofia ;

Ma, Heng ;

Trifan, Anda ;

Ramanathan, Arvind ;

Brettin, Tom ;

Partin, Alexander ;

Xia, Fangfang ;

Duan, Xiaotan ;

Stevens, Rick ;

Coveney, Peter V. .

INTERFACE FOCUS, 2021, 11 (06)

[6]

Biamonte J, 2017, Arxiv, DOI arXiv:1708.00006

[7]

Budinski L, 2022, Arxiv, DOI arXiv:2103.03804

[8]

Das Sarma S, 2022, MIT Technol Rev

[9] Finding flows of a Navier-Stokes fluid through quantum computing [J].

Gaitan, Frank .

NPJ QUANTUM INFORMATION, 2020, 6 (01)

[10]

Gourianov N, 2022, Arxiv, DOI arXiv:2106.05782

← 1 2 3 4 →