From the 1 of 9 linked papers with an AI index.
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Quantum model reduction based on Oja's flow
Miguel Casanova, Kentaro Ohki, Francesco Ticozzi
The paper introduces two non‑perturbative algorithms based on Oja's continuous‑time principal component flow to obtain reduced dynamical models for Markovian open quantum systems,…
Reconstructing Quantum States and Expectations via Dynamical Tomography
Marco Peruzzo, Tommaso Grigoletto, Francesco Ticozzi
When the dynamics of a quantum system of interest is known, an informationally-complete set of observables is not needed for state reconstruction via tomographic techniques: lettin…
Quantum model reduction for continuous-time quantum filters
Tommaso Grigoletto, Clément Pellegrini, Francesco Ticozzi
The use of quantum stochastic models is widespread in dynamical reduction, simulation of open systems, feedback control and adaptive estimation. In many applications only part of t…
Reconstructing Quantum States from Local Observation: A Dynamical Viewpoint
Marco Peruzzo, Tommaso Grigoletto, Francesco Ticozzi
We analyze the problem of reconstructing an unknown quantum state of a multipartite system from repeated measurements of local observables. In particular, via a system-theoretic ob…
Finding Quantum Codes via Riemannian Optimization
Miguel Casanova, Kentaro Ohki, Francesco Ticozzi
We propose a novel optimization scheme designed to find optimally correctable subspace codes for a known quantum noise channel. To each candidate subspace code we first associate a…