Quick Navigation
Topics
Quantum Simulation
Scalable suppression of heating errors in large trapped-ion quantum processors
arXiv
Authors: Zixuan Huo, Yangchao Shen, Xiao Yuan, Xiao-Ming Zhang
Year
2026
Paper ID
76163
Status
Preprint
Abstract Read
~2 min
Abstract Words
200
Citations
N/A
Abstract
Trapped-ion processors are leading candidates for scalable quantum computation. However, motional heating remains a key obstacle to fault-tolerant operation, especially when system size increases. Heating error is particularly challenging to suppress due to is incoherence nature, and no general methods currently exist for mitigating their impact in large systems with multiple phonon modes. In this work, based on a careful analysis about the dependence of heating-induced infidelity on phase-space trajectories, we present a simple yet comprehensive framework for suppressing heating errors in large trapped-ion quantum processors. Our approach is flexible, allowing various control pulse bases, ion numbers, and noise levels. Our approach is also compatible with existing error-mitigation techniques, including those targeting laser phase and frequency noise. Crucially, it relies on an efficiently computable cost function that avoids the exponential overhead of full fidelity estimation. We perform numerical simulations for systems with up to 55 qubits, demonstrating up to a fivefold reduction in infidelities compared with the conventional method in typical regimes. We further show that our framework simultaneously reduces the sensitivity of the gate rotation angle to detuning errors, leading to an improvement in detuning-error tolerance. These results offer a practical route toward robust, large-scale quantum computation with trapped ions.
Why This Paper Matters
- This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Trapped-ion processors are leading candidates for scalable quantum computation.
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.