Relative entropy optimization and its applications
V Chandrasekaran, P Shah - Mathematical Programming, 2017 - Springer
In this expository article, we study optimization problems specified via linear and relative
entropy inequalities. Such relative entropy programs (REPs) are convex optimization …
entropy inequalities. Such relative entropy programs (REPs) are convex optimization …
Robust self-testing of quantum systems via noncontextuality inequalities
Characterizing unknown quantum states and measurements is a fundamental problem in
quantum information processing. In this Letter, we provide a novel scheme to self-test local …
quantum information processing. In this Letter, we provide a novel scheme to self-test local …
Machine learning pipeline for quantum state estimation with incomplete measurements
Two-qubit systems typically employ 36 projective measurements for high-fidelity
tomographic estimation. The overcomplete nature of the 36 measurements suggests …
tomographic estimation. The overcomplete nature of the 36 measurements suggests …
Maximal entropy approach for quantum state tomography
Quantum computation has been growing rapidly in both theory and experiments. In
particular, quantum computing devices with a large number of qubits have been developed …
particular, quantum computing devices with a large number of qubits have been developed …
Communication games reveal preparation contextuality
A communication game consists of distributed parties attempting to jointly complete a task
with restricted communication. Such games are useful tools for studying limitations of …
with restricted communication. Such games are useful tools for studying limitations of …
A modified least squares-based tomography with density matrix perturbation and linear entropy consideration along with performance analysis
S Mondal, AK Dutta - New Journal of Physics, 2023 - iopscience.iop.org
Quantum state tomography identifies target quantum states by performing repetitive
measurements on identical copies. In this paper, we have two key contributions aimed at …
measurements on identical copies. In this paper, we have two key contributions aimed at …
Deep learning-based quantum state tomography with imperfect measurement
C Pan, J Zhang - International Journal of Theoretical Physics, 2022 - Springer
In recent years, neural network estimator-based quantum state tomography has gained its
popularity. Inspired by advances in the field of state-of-the-art deep learning techniques, we …
popularity. Inspired by advances in the field of state-of-the-art deep learning techniques, we …
A projected gradient method for optimization over density matrices
DS Gonçalves, MA Gomes-Ruggiero… - … Methods and Software, 2016 - Taylor & Francis
An ensemble of quantum states can be described by a Hermitian, positive semidefinite and
unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain …
unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain …
On how neural networks enhance quantum state tomography with limited resources
Quantum state tomography is defined as a process of reconstructing the density matrix of a
quantum state and is an important task for various emerging quantum technologies. In this …
quantum state and is an important task for various emerging quantum technologies. In this …
Single top squark production as a probe of natural supersymmetry at the LHC
K Hikasa, J Li, L Wu, JM Yang - Physical Review D, 2016 - APS
Light top squarks (stops) and light higgsinos are the key features of natural supersymmetry
(SUSY), where the higgsinos χ˜ 1±and χ˜ 1, 2 0 are nearly degenerate and act as the …
(SUSY), where the higgsinos χ˜ 1±and χ˜ 1, 2 0 are nearly degenerate and act as the …