Quick Navigation
Topics
Trapped Ion Quantum Computing
Quantum Simulation
Efficient tensor network simulation of multi-emitter non-Markovian systems
arXiv
Authors: Irene Papaefstathiou, Daniel Malz, J. Ignacio Cirac, Mari Carmen Bañuls
Year
2024
Paper ID
65416
Status
Preprint
Abstract Read
~2 min
Abstract Words
171
Citations
N/A
Abstract
We present a numerical method to simulate a system of multiple emitters coupled to a non-interacting bath, in any parameter regime. Our method relies on a Block Lanczos transformation that maps the whole system onto a strip geometry, whose width is given by the number of emitters. Utilizing the spatial symmetries of the problem and identifying the relevant range of energies of the bath we achieve a more efficient simulation, which we perform using tensor network techniques. As a demonstration, we study the collective emission from multiple emitters coupled to a square lattice of bosons and observe how the departure from Markovianity as coupling strength and emitter number is increased prevents collective radiation. We also simulate the dynamic preparation of an excitation in a bound state from a multi-excitation initial state. Our work opens new possibilities for the systematic exploration of non-Markovian effects in the dynamics and equilibrium properties of multi-emitter systems. Furthermore, it can easily be extended to other setups, including finite bath temperature or impurities coupled to fermionic environments.
Why This Paper Matters
- This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- We present a numerical method to simulate a system of multiple emitters coupled to a non-interacting bath, in any parameter regime.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.