Quick Navigation
Topics
Trapped Ion Quantum Computing
Superconducting Qubits
Scalable Fermionic Error Correction in Majorana Surface Codes
arXiv
Authors: Oscar Viyuela, Sagar Vijay, Liang Fu
Year
2018
Paper ID
22570
Status
Preprint
Abstract Read
~2 min
Abstract Words
160
Citations
N/A
Abstract
We study the error correcting properties of Majorana Surface Codes (MSC), topological quantum codes constructed out of interacting Majorana fermions, which can be used to store quantum information and perform quantum computation. These quantum memories suffer from purely "fermionic" errors, such as quasiparticle poisoning (QP), that have no analog in conventional platforms with bosonic qubits. In physical realizations where QP dominates, we show that errors can be corrected provided that the poisoning rate is below a threshold of sim11\%. When QP is highly suppressed and fermionic bilinear ("bosonic") errors become dominant, we find an error threshold of sim16\%, which is much higher than the threshold for spin-based topological memories like the Surface code or the Color code. In addition, we derive new lattice gauge theories to account for measurement errors. These results, together with the inherent error suppression provided by the superconducting gap in physical realizations of the MSC, makes this a strong candidate for a robust topological quantum memory.
Why This Paper Matters
- This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
- It adds a 2018 reference point for readers tracking recent quantum research.
- We study the error correcting properties of Majorana Surface Codes (MSC), topological quantum codes constructed out of interacting Majorana fermions, which can be used to store...
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.