8 citations · 12 across the 7 of their papers we have counts for
7 papers
A Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography
Shakir Showkat Sofi, Charlotte Vermeylen, Fatemeh Mohammadi +1
Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantu…
Tomography by Design: An Algebraic Approach to Low-Rank Quantum States
Shakir Showkat Sofi, Charlotte Vermeylen, Lieven De Lathauwer
We present an algebraic algorithm for quantum state tomography that leverages measurements of certain observables to estimate structured entries of the underlying density matrix. U…
Tensor Train Completion from Fiberwise Observations Along a Single Mode
Shakir Showkat Sofi, Lieven De Lathauwer
Tensor completion is an extension of matrix completion aimed at recovering a multiway data tensor by leveraging a given subset of its entries (observations) and the pattern of obse…
Tensor Train Quantum State Tomography using Compressed Sensing
Shakir Showkat Sofi, Charlotte Vermeylen, Lieven De Lathauwer
Quantum state tomography (QST) is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of…
Some contributions to Lagrangian modelling of Power Converters
Shakir Showkat Sofi, Mosaib Ul Munieeb, Fazil Bashir +2
Lagrangian modelling can be used to derive mathematical models for complex power electronic converters. This approach uses scalar quantities (kinetic and potential energy) to deriv…
A case study of spatiotemporal forecasting techniques for weather forecasting
Shakir Showkat Sofi, Ivan Oseledets
The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most essential processes…
Image Segmentation with Topological Priors
Shakir Showkat Sofi, Nadezhda Alsahanova
Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors both before and during the deep neu…