2025-optimization-course
Repository on semidefinite programming in quantum information for the course "Optimization methods for quantum information"
Science Score: 44.0%
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Repository
Repository on semidefinite programming in quantum information for the course "Optimization methods for quantum information"
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README.md
2025-optimization-course
Repository on semidefinite programming in quantum information for the course "Optimization methods for quantum information" (year 2025) held at the University of Naples Federico II.
This is a work in progress.
The codes are implemented in Python 3 using libraries such as numpy, matplotlib, cvxpy (mosek solver), scipy, time, IPython.
License: All notebooks are available under the terms of the Creative Commons CC-BY 4.0 public license.
SUMMARY ON THE TUTORIALS
First encounter with SDPs: ground state energy calculation as an SDP (non- scalable)
Ubiquitous SDPs: energy gap as an SDP; Helstrom bound as an SDP.
Certification of quantum properties under partial information. SDP for lower bound on the fidelity of state preparation; Lower bound for purity (Schur com- plement to obtain an SDP representation).
SDP relaxations via moment matrix: scalable methods for many-body systems. Lower bound for ground state energy of the transverse field Ising model as an illustrative example.
Owner
- Login: donatofarina91
- Kind: user
- Repositories: 1
- Profile: https://github.com/donatofarina91
Citation (citation.bib)
@misc{farina_2025_sdp_tutorial,
author = {Donato Farina},
title = {2025-SDP-tutorial},
year = {2025},
howpublished = {\url{https://github.com/donatofarina91/2025-optimization-course}},
note = {If you use this software, please cite it using the metadata from this file.},
keywords = {semidefinite programming, quantum information, many-body systems},
license = {CC-BY-4.0},
url = {https://github.com/donatofarina91/2025-optimization-course},
author_orcid = {0000-0002-7248-664X}
}
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