Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Adaptive variational low-rank dynamics for open quantum systems
 
Loading...
Thumbnail Image
research article

Adaptive variational low-rank dynamics for open quantum systems

Gravina, Luca  
•
Savona, Vincenzo  
April 19, 2024
Physical Review Research

We introduce a model-independent method for the efficient simulation of low-entropy systems, whose dynamics can be accurately described with a limited number of states. Our method leverages the time-dependent variational principle to efficiently integrate the Lindblad master equation, dynamically identifying and modifying the low-rank basis over which we decompose the system's evolution. By dynamically adapting the dimension of this basis, and thus the rank of the density matrix, our method maintains optimal representation of the system state, offering a substantial computational advantage over existing adaptive low-rank schemes in terms of both computational time and memory requirements. We demonstrate the efficacy of our method through extensive benchmarks on a variety of model systems, with a particular emphasis on multiqubit bosonic codes, a promising candidate for fault-tolerant quantum hardware. Our results highlight the method's versatility and efficiency, making it applicable to a wide range of systems characterized by arbitrary degrees of entanglement and moderate entropy throughout their dynamics. We provide an implementation of the method as a Julia package, making it readily available to use.

  • Details
  • Metrics
Type
research article
DOI
10.1103/PhysRevResearch.6.023072
Web of Science ID

WOS:001217855800004

Author(s)
Gravina, Luca  
•
Savona, Vincenzo  
Date Issued

2024-04-19

Publisher

Amer Physical Soc

Published in
Physical Review Research
Volume

6

Issue

2

Article Number

023072

Subjects

Physical Sciences

•

Error-Correction

•

Tensor Networks

•

States

•

Generation

•

Operators

•

Light

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTPN  
FunderGrant Number

Swiss National Science Foundation (SNSF)

200020_185015

EPFL Science Seed Fund

563276 2021

Available on Infoscience
June 5, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/208301
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés