Quick Navigation
Topics
Trapped Ion Quantum Computing
Superconducting Qubits
Fast generation of Cat states in Kerr nonlinear resonators via optimal adiabatic control
arXiv
Authors: Jiao-Jiao Xue, Ke-Hui Yu, Wen-Xiao Liu, Xin Wang, Hong-Rong Li
Year
2021
Paper ID
61970
Status
Preprint
Abstract Read
~2 min
Abstract Words
195
Citations
N/A
Abstract
Macroscopic cat states have been widely studied to illustrate fundamental principles of quantum physics as well as their application in quantum information processing. In this paper, we propose a quantum speedup method for adiabatic creation of cat states in a Kerr nonlinear resonator via gradient-descent optimal adiabatic control. By simultaneously adiabatic tuning the the cavity detuning and driving field strength, the width of minimum energy gap between the target trajectory and non-adiabatic trajectory can be widen, which allows us to speed up the evolution along the adiabatic path. Compared with the previous proposal of preparing the cat state by only controlling two-photon pumping strength in a Kerr nonlinear resonator, our method can prepare the target state with much shorter time, as well as a high fidelity and a large non-classical volume. It is worth noting that the cat state prepared by our method is also robust against single-photon loss very well. Moreover, when our proposal has a large initial detuning, it will creates a large-size cat state successfully. This proposal of preparing cat states can be implemented in superconducting quantum circuits, which provides a quantum state resource for quantum information encoding and fault-tolerant quantum computing.
Paper Tools
Show Paper
arXiv
Publisher
Sign in to cite
Sign in to compare
Sign in to copy DOI
Add to Reading List
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.