Probabilistic and approximate universal quantum purification machines
Zoe G. del Toro, Jessica Bavaresco

TL;DR
This paper investigates quantum purification machines, analyzing their limitations and performance in probabilistic and approximate settings, revealing fundamental obstructions and optimal strategies.
Contribution
It formalizes quantum purification tasks, proves the impossibility of universal probabilistic purification, and derives analytical bounds for approximate purification strategies.
Findings
Universal probabilistic purification is impossible with finite copies.
Optimal approximate strategies depend on environment dimension.
Append-environment strategies outperform others at small environment dimensions.
Abstract
We study the task of lifting arbitrary quantum states and channels to purifications and Stinespring dilations, respectively, in both the probabilistic exact and deterministic approximate settings. We formalize this task through a general framework of quantum purification machines that, given a finite number of copies or uses of a black-box input, aim to output a corresponding purification or Stinespring dilation. In the probabilistic exact setting, we show that universality is not necessary to rule out such transformations: the simple requirement that a machine purifies two inputs of different rank with non-zero probability already implies that it cannot be described by a linear positive map. This simple argument captures a fundamental obstruction of quantum theory and recovers the impossibility of universal probabilistic purification from finitely many copies. In the approximate…
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