Models of reduced-noise, probabilistic linear amplifiers

TL;DR

This paper introduces a flexible model for linear amplifiers that interpolates between ideal, nonideal, and probabilistic amplification, analyzing their noise, fidelity, and signal-to-noise performance.

Contribution

The authors develop a cascaded amplifier model that unifies deterministic and nondeterministic linear amplifiers, including nonideal variants, with detailed performance analysis.

Findings

The model can realize ideal and nonideal amplification depending on noise parameter μ^2.

Performance metrics such as gain-corrected fidelity and signal-to-noise ratio are evaluated.

Probabilistic operation improves certain amplification qualities over deterministic counterparts.

Abstract

We construct an amplifier that interpolates between a nondeterministic, immaculate linear amplifier and a deterministic, ideal linear amplifier and beyond to nonideal linear amplifiers. The construction involves cascading an immaculate linear amplifier that has amplitude gain $g_1$ with a (possibly) nonideal linear amplifier that has gain $g_2$. With respect to normally ordered moments, the device has output noise $\mu^2(G^2-1)$ where $G=g_1 g_2$ is the overall amplitude gain and $\mu^2$ is a noise parameter. When $\mu^2\ge1$, our devices realize ideal ($\mu^2=1$) and nonideal ($\mu^2>1$) linear amplifiers. When $0\le\mu^2<1$, these devices work effectively only over a restricted region of phase space and with some subunity success probability $p_{\checkmark}$. We investigate the performance of our $\mu^2$-amplifiers in terms of a gain-corrected probability-fidelity product and the ratio of input to output signal-to-noise ratios corrected for success probability.