Finite quantum tomography via semidefinite programming
M. A. Jafarizadeh, M. Mirzaee, M. Rezaee
TL;DR
This paper presents a method for finite quantum state tomography using semidefinite programming, applicable to various quantum systems, and confirms its results align with previous studies.
Contribution
It introduces a semidefinite programming approach for finite quantum tomography across multiple quantum state types, enhancing existing methods.
Findings
Results agree with previous studies
Applicable to qudit, N-qubit, phase, and spin states
Demonstrates effectiveness of convex optimization in quantum tomography
Abstract
Using the the convex semidefinite programming method and superoperator formalism we obtain the finite quantum tomography of some mixed quantum states such as: qudit tomography, N-qubit tomography, phase tomography and coherent spin state tomography, where that obtained results are in agreement with those of References \cite{schack,Pegg,Barnett,Buzek,Weigert}.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.