Quantum process tomography with Heisenberg scaling based on Gaussian state and binary detection

TL;DR

This paper introduces a quantum process tomography method using two-mode squeezed vacuum and binary detection to achieve Heisenberg scaling in parameter estimation, analyzing its performance under realistic loss and noise conditions.

Contribution

It presents a novel quantum process tomography scheme leveraging Gaussian states and binary detection to attain Heisenberg scaling, including analysis of practical loss and noise effects.

Findings

Achieves Heisenberg scaling in parameter estimation

Analyzes impact of photon loss and thermal noise

Provides quantum Fisher information calculations

Abstract

We propose a quantum process tomography scheme that utilizes two-mode squeezed vacuum to realize the parameter estimation with Heisenberg scaling. The objective is to estimate a rotating angle of polarization and parity detection is used as the detection strategy. With the help of symplectic matrix theory, we discuss the estimation visibility and sensitivity of output signal in lossless situation, the quantum Fisher information is also given via calculation. Finally, the impacts of two realistic factors on both visibility and sensitivity are also considered, including photon loss during the input generation, and photon loss along with thermal noise during the output detection.