Approximating projections by quantum operations
Roy Araiza, Colton Griffin, Aneesh Khilnani, Thomas Sinclair
TL;DR
This paper explores methods to approximate projections with quantum channels using semidefinite programming, introducing invariants linked to the quantum Lovász theta function to analyze matricial subsystems.
Contribution
It introduces two invariants of matricial subsystems related to the quantum Lovász theta function, advancing the understanding of quantum channel approximation.
Findings
Derived invariants for matricial subsystems.
Connected invariants to the quantum Lovász theta function.
Provided a semidefinite programming approach for approximation.
Abstract
Using techniques from semidefinite programming, we study the problem of finding a closest quantum channel to the projection onto a matricial subsystem. We derive two invariants of matricial subsystems which are related to the quantum Lov\'asz theta function of Duan, Severini, and Winter.
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Complexity and Algorithms in Graphs · Quantum Information and Cryptography