Quick Navigation
Topics
Trapped Ion Quantum Computing
High-Fidelity Universal Quantum Gate Compilation for Non-semisimple Ising Anyons via Genetic Algorithm-Optimized Solovay-Kitaev Decomposition
arXiv
Authors: Jiangwei Long, Zihui Liu, Yizhi Li, Jianxin Zhong, Lijun Meng
Year
2025
Paper ID
17050
Status
Preprint
Abstract Read
~2 min
Abstract Words
136
Citations
N/A
Abstract
We present a systematic numerical construction of a universal quantum gate set for topological quantum computation based on the non-semisimple Ising anyons model. By employing a Genetic Algorithm-enhanced Solovay-Kitaev Algorithm (GA-enhanced SKA), we achieve high-fidelity approximations of standard single-qubit gates (Hadamard H-gate and phase T-gate) with a recursion level of just three, meeting the fidelity requirements for fault-tolerant quantum computation. Our numerical results demonstrate that for the critical parameter range α \in [2.001, 2.022], a few braiding operations can approximate the local equivalence class [CNOT] with high precision. Specifically, at α =2.012, 2.015, 2.020, and 2.022, we successfully construct a universal gate set {H, T, CNOT} with leakage errors of two-qubit gate below 0.07,0.08,0.09 and 0.10, respectively. This work establishes a new pathway towards universal quantum computation using non-semisimple Ising anyons, overcoming the limitations of traditional Ising models through optimized braiding sequences and Genetic Algorithm-driven compilation.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We present a systematic numerical construction of a universal quantum gate set for topological quantum computation based on the non-semisimple Ising anyons model.
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.