13,106 research outputs found
Maximum-likelihood algorithm for quantum tomography
Optical homodyne tomography is discussed in the context of classical image
processing. Analogies between these two fields are traced and used to formulate
an iterative numerical algorithm for reconstructing the Wigner function from
homodyne statistics.Comment: 7 pages LaTeX, contribution to proceedings of 6th central-european
workshop on quantum optic
Image processing as state reconstruction in optics
The image reconstruction of partially coherent light is interpreted as the
quantum state reconstruction. The efficient method based on maximum-likelihood
estimation is proposed to acquire information from registered intensity
measurements affected by noise. The connection with totally incoherent image
restoration is pointed out. The feasibility of the method is demonstrated
numerically. Spatial and correlation details significantly smaller than the
diffraction limit are revealed in the reconstructed pattern.Comment: 10 pages, 5 figure
Iterative algorithm for reconstruction of entangled states
An iterative algorithm for the reconstruction of an unknown quantum state
from the results of incompatible measurements is proposed. It consists of
Expectation-Maximization step followed by a unitary transformation of the
eigenbasis of the density matrix. The procedure has been applied to the
reconstruction of the entangled pair of photons.Comment: 4 pages, no figures, some formulations changed, a minor mistake
correcte
Randomized Row and Column Iterative Methods with a Quantum Computer
We consider the quantum implementations of the two classical iterative
solvers for a system of linear equations, including the Kaczmarz method which
uses a row of coefficient matrix in each iteration step, and the coordinate
descent method which utilizes a column instead. These two methods are widely
applied in big data science due to their very simple iteration schemes. In this
paper we use the block-encoding technique and propose fast quantum
implementations for these two approaches, under the assumption that the quantum
states of each row or each column can be efficiently prepared. The quantum
algorithms achieve exponential speed up at the problem size over the classical
versions, meanwhile their complexity is nearly linear at the number of steps
Painless Breakups -- Efficient Demixing of Low Rank Matrices
Assume we are given a sum of linear measurements of different rank-
matrices of the form . When and under
which conditions is it possible to extract (demix) the individual matrices
from the single measurement vector ? And can we do the demixing
numerically efficiently? We present two computationally efficient algorithms
based on hard thresholding to solve this low rank demixing problem. We prove
that under suitable conditions these algorithms are guaranteed to converge to
the correct solution at a linear rate. We discuss applications in connection
with quantum tomography and the Internet-of-Things. Numerical simulations
demonstrate empirically the performance of the proposed algorithms
Recommended from our members
Atomic electron tomography in three and four dimensions
Atomic electron tomography (AET) has become a powerful tool for atomic-scale structural characterization in three and four dimensions. It provides the ability to correlate structures and properties of materials at the single-atom level. With recent advances in data acquisition methods, iterative three-dimensional (3D) reconstruction algorithms, and post-processing methods, AET can now determine 3D atomic coordinates and chemical species with sub-Angstrom precision, and reveal their atomic-scale time evolution during dynamical processes. Here, we review the recent experimental and algorithmic developments of AET and highlight several groundbreaking experiments, which include pinpointing the 3D atom positions and chemical order/disorder in technologically relevant materials and capturing how atoms rearrange during early nucleation at four-dimensional atomic resolution
Diluted maximum-likelihood algorithm for quantum tomography
We propose a refined iterative likelihood-maximization algorithm for
reconstructing a quantum state from a set of tomographic measurements. The
algorithm is characterized by a very high convergence rate and features a
simple adaptive procedure that ensures likelihood increase in every iteration
and convergence to the maximum-likelihood state.
We apply the algorithm to homodyne tomography of optical states and quantum
tomography of entangled spin states of trapped ions and investigate its
convergence properties.Comment: v2: Convergence proof adde
Iterative maximum-likelihood reconstruction in quantum homodyne tomography
I propose an iterative expectation maximization algorithm for reconstructing
a quantum optical ensemble from a set of balanced homodyne measurements
performed on an optical state. The algorithm applies directly to the acquired
data, bypassing the intermediate step of calculating marginal distributions.
The advantages of the new method are made manifest by comparing it with the
traditional inverse Radon transformation technique
- âŠ