Disturbance Evaluation Circuit in Quantum Measurement

TL;DR

This paper introduces a new method for evaluating quantum measurement disturbance, demonstrating its effectiveness through simulation and experiments, and comparing it with existing approaches to improve understanding in quantum physics and technologies.

Contribution

A novel evaluation method for QRMS disturbance that links it to the second-order derivative of decoherence, outperforming existing methods in accuracy and applicability.

Findings

The new method accurately captures disturbance features.

It performs comparably or better than TSM and WMM in experiments.

The approach offers deeper insights into measurement disturbance evaluation.

Abstract

According to the uncertainty principle, every quantum measurement accompanies disturbance. In particular, accurate sequential measurements need the accurate control of disturbance. However, the correct role of disturbance in the uncertainty principle has been known only recently. Understanding the disturbance is crucial for understanding the fundamentals of physics, and accurately evaluating the disturbance is important for quantum technologies such as quantum information processing and quantum metrology. Therefore, the experimental evaluation of the disturbance is a significant challenge in those fields. In this study, we propose a novel evaluation method for the quantum root-mean-square (QRMS) disturbance and compare its performance with the existing approaches, known as the three-state method (TSM) and the weak measurement method (WMM). Our method establishes a correspondence between the QRMS disturbance of the measurement and the second-order derivative of the decoherence induced in a newly introduced weak probe system with respect to the coupling strength of the weak interaction at its zero-limit. Furthermore, we demonstrate the effectiveness of our method in comparison with the other two through a simulation and experiment using a quantum computer. The results capture the key features of the TSM, WMM, and our method, providing insights into the strengths and limitations of these methods.