A robust compressive quantum state tomography algorithm using ADMM
2014 (English)In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 6878-6883 p.Conference paper (Refereed)
The possible state space dimension increases exponentially with respect to the number of qubits. This feature makes the quantum state tomography expensive and impractical for identifying the state of merely several qubits. The recent developed approach, compressed sensing, gives us an alternative to estimate the quantum state with fewer measurements. It is proved that the estimation then can be converted to a convex optimization problem with quantum mechanics constraints. In this paper we present an alternating augmented Lagrangian method for quantum convex optimization problem aiming to recover pure or near pure quantum states corrupted by sparse noise given observables and the expectation values of the measurements. The proposed algorithm is much faster, robust to outlier noises (even very large for some entries) and can solve the reconstruction problem distributively. The simulations verify the superiority of the proposed algorithm and compare it to the conventional least square and compressive quantum tomography using the Dantzig method.
Place, publisher, year, edition, pages
2014. 6878-6883 p.
ADMM, Convex optimization and regularization, Quantum state tomography, Rank minimization
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-175127DOI: 10.3182/20140824-6-ZA-1003.01815ScopusID: 2-s2.0-84929783485ISBN: 9783902823625OAI: oai:DiVA.org:kth-175127DiVA: diva2:861738
19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014, 24 August 2014 through 29 August 2014
QC 201510192015-10-192015-10-092015-10-19Bibliographically approved