Generating Non-Decomposable Maps with Differentiable Semidefinite Programming

Fuente: arXiv
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Main Authors: Morgillo, Angela Rosy, Poderini, Davide, Anselmi, Fabio, Benatti, Fabio, Sacchi, Massimiliano F., Macchiavello, Chiara
Format: Preprint
Published: 2026
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_version_ 1866910221272612864
author Morgillo, Angela Rosy
Poderini, Davide
Anselmi, Fabio
Benatti, Fabio
Sacchi, Massimiliano F.
Macchiavello, Chiara
author_facet Morgillo, Angela Rosy
Poderini, Davide
Anselmi, Fabio
Benatti, Fabio
Sacchi, Massimiliano F.
Macchiavello, Chiara
contents Positive maps that are not decomposable are a key resource in entanglement theory because they can detect bound entangled states, yet systematic methods for constructing them remain limited. We introduce an optimization framework based on differentiable semidefinite programming (SDP) for generating positive non-decomposable maps under flexible structural constraints on their Choi matrices. The method combines SDP-based certificates of non-decomposability and positivity with gradient-based optimization, enabling a systematic search over maps with different input and output dimensions. Within this framework, we generate previously unknown numerical examples, identify a parametrized family of maps arising from masked Choi matrices, and construct real non-decomposable maps. We further show that the same approach can be adapted to explore open questions in quantum information theory, including the PPT square conjecture and recently proposed eigenvalue bounds for 2-positive trace-preserving maps.
format Preprint
id arxiv_https___arxiv_org_abs_2605_14644
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Generating Non-Decomposable Maps with Differentiable Semidefinite Programming
Morgillo, Angela Rosy
Poderini, Davide
Anselmi, Fabio
Benatti, Fabio
Sacchi, Massimiliano F.
Macchiavello, Chiara
Quantum Physics
Positive maps that are not decomposable are a key resource in entanglement theory because they can detect bound entangled states, yet systematic methods for constructing them remain limited. We introduce an optimization framework based on differentiable semidefinite programming (SDP) for generating positive non-decomposable maps under flexible structural constraints on their Choi matrices. The method combines SDP-based certificates of non-decomposability and positivity with gradient-based optimization, enabling a systematic search over maps with different input and output dimensions. Within this framework, we generate previously unknown numerical examples, identify a parametrized family of maps arising from masked Choi matrices, and construct real non-decomposable maps. We further show that the same approach can be adapted to explore open questions in quantum information theory, including the PPT square conjecture and recently proposed eigenvalue bounds for 2-positive trace-preserving maps.
title Generating Non-Decomposable Maps with Differentiable Semidefinite Programming
topic Quantum Physics
url https://arxiv.org/abs/2605.14644