Quick Navigation
Topics
Quantum Error Correction Fault Tolerance
Superconducting Qubits
Developments in superconducting erasure qubits for hardware-efficient quantum error correction
arXiv
Authors: Maria Violaris, Luciana Henaut, James Wills, Gioele Consani, Jamie Friel, Brian Vlastakis
Year
2026
Paper ID
4211
Status
Preprint
Abstract Read
~2 min
Abstract Words
130
Citations
N/A
Abstract
Quantum computers are inherently noisy, and a crucial challenge for achieving large-scale, fault-tolerant quantum computing is to implement quantum error correction. A promising direction that has made rapid recent progress is to design hardware that has a specific noise profile, leading to a significantly higher threshold for noise with certain quantum error correcting codes. This Perspective focuses on erasure qubits, which enable hardware-efficient quantum error correction, by concatenating an inner code built-in to the hardware with an outer code. We focus on implementations of dual-rail encoded erasure qubits using superconducting qubits, giving an overview of recent developments in theory and simulation, and hardware demonstrators. We also discuss the differences between implementations; near-term applications using quantum error detection; and the open problems for developing this approach towards early fault-tolerant quantum computers.
Why This Paper Matters
- This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Quantum computers are inherently noisy, and a crucial challenge for achieving large-scale, fault-tolerant quantum computing is to implement quantum error correction.
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.