Quick Navigation
Topics
Open Quantum Systems Decoherence
Quantum Machine Learning
Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
arXiv
Authors: Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli, Elisabetta Paladino, Giuseppe A. Falci
Year
2025
Paper ID
36059
Status
Preprint
Abstract Read
~2 min
Abstract Words
53
Citations
N/A
Abstract
We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critical issues of the approach are discussed.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems.
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.