Dynamical uncertainty propagation with noisy quantum parameters

TL;DR

This paper introduces a novel method for propagating uncertainties in quantum dynamics simulations, which is faster than Monte Carlo methods and provides detailed insights into individual parameter effects, validated with IBM quantum computer experiments.

Contribution

A new direct uncertainty propagation method for quantum dynamics that is computationally efficient and reveals individual parameter impacts, surpassing Monte Carlo approaches.

Findings

Method is faster than Monte Carlo simulations.

Provides detailed influence of each uncertainty parameter.

Validated with IBM quantum computer experiments.

Abstract

Many quantum technologies rely on high-precision dynamics, which raises the question of how these are influenced by the experimental uncertainties that are always present in real-life settings. A standard approach in the literature to assess this is Monte Carlo sampling, which suffers from two major drawbacks. First, it is computationally expensive. Second, it does not reveal the effect that each individual uncertainty parameter has on the state of the system. In this work, we evade both these drawbacks by incorporating propagation of uncertainty directly into simulations of quantum dynamics, thereby obtaining a method that is faster than Monte Carlo simulations and directly provides information on how each uncertainty parameter influence the system dynamics. Additionally, we compare our method to experimental results obtained using the IBM quantum computers.