We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-
QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian …

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by
detailing the structures at resolutions beyond the limits of imaging or measuring tools. These …

By codesigning a metaoptical front end in conjunction with an image-processing back end,
we demonstrate noise sensitivity and compactness substantially superior to either an optics …

Abstract Recently Chen and Poor initiated the study of learning mixtures of linear dynamical
systems. While linear dynamical systems already have wide-ranging applications in …

Leverage score sampling is a powerful technique that originates from theoretical computer
science, which can be used to speed up a large number of fundamental questions, eg linear …

Subspace-based signal processing techniques, such as the Estimation of Signal Parameters
via Rotational Invariant Techniques (ESPRIT) algorithm, are popular methods for spectral …

Resolving a linear combination of point sources from their Fourier data in a bounded domain
is a fundamental problem in imaging and signal processing. With incomplete Fourier data …

We revisit the problem of learning mixtures of spherical Gaussians. Given samples from a
mixture $\frac {1}{k}\sum_ {j= 1}^{k}\mathcal {N}(\mu_j, I_d) $, the goal is to estimate the …

Performance assessment of an imaging system is important for the optimization design with
various technologies. The information-theoretic viewpoint based on communication theory …

In this paper, we analyze the capacity of super-resolution (SR) of one-dimensional positive
sources. In particular, we consider a similar setting as in Batenkov et al.(2020, Inf. Inference …