Quick Navigation
Topics
Trapped Ion Quantum Computing
Physics-Informed Optimisation of Conveyor Mode Spin Qubit Transport
arXiv
Authors: Andrii Sokolov, Conor Power, Elena Blokhina
Year
2025
Paper ID
51639
Status
Preprint
Abstract Read
~2 min
Abstract Words
137
Citations
N/A
Abstract
Scalable quantum information processing in spin-based architectures necessitates the a bility to reliably shuttle quantum states across extended device regions with minimal decoherence. In this work, we present a physics-informed algorithm for optimizing electrostatic bias equences that enable conveyor-mode electron transport in silicon-based quantum dot devices. Our approach combines self-consistent Poisson and Schrodinger solvers to maintain a constant ground state energy and enable near-constant velocity shuttling, with potential applicability to both single-electron and hole transport. We validate the algorithm across three representative technologies: Fully-Depleted Silicon on Insulator (FD-SOI), Silicon Metal-Oxide-Seminconductor (SiMOS) and Silicon-Germanium Heterostracture (Si/SiGe), highlighting key limitations and material-specific effects that influence transport fidelity. Our findings underscore the impact of gate geometry, dielectric interfaces, and quantum dot size on the stability of shuttling operations, and offer pathways toward improving coherence preservation in large-scale quantum systems.
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.
- Scalable quantum information processing in spin-based architectures necessitates the a bility to reliably shuttle quantum states across extended device regions with minimal...
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.