The impact of noise on the simulation of NMR spectroscopy on NISQ devices

TL;DR

This paper explores how noise affects the simulation of NMR spectroscopy on NISQ quantum computers, proposing a method to quantify noise tolerance and improve application fidelity on current noisy devices.

Contribution

It introduces an effective decoherence rate for NMR simulations on NISQ devices, using simple fidelity metrics to assess noise impact and guide practical quantum applications.

Findings

Noise significantly impacts NMR spectral accuracy on NISQ devices

Effective decoherence rate can be estimated with fidelity metrics

Simulation results inform noise tolerance thresholds for quantum algorithms

Abstract

With the surge of quantum computing platforms that continue to push the boundaries of capabilities of noisy intermediate-scale quantum computers, there is a growing interest in finding relevant applications and quantifying the corresponding error budgets. We present a simulation of nuclear magnetic resonance (NMR) spectroscopy of small organic molecules on publicly available cloud quantum computers. We are using two quantum computing platforms, namely IBM's quantum processors based on superconducting qubits and IonQ's Aria trapped ion quantum computer addressed via Amazon Braket. We analyze the impact of noise on the obtained NMR spectra, and we formulate an effective decoherence rate that quantifies the threshold noise that our proposed algorithm can tolerate. We show that the effective decoherence rate can be calculated using simple fidelity metrics that are available by cloud quantum computing providers. Our investigation paves the way to better employ such application-driven quantum tasks on current noisy quantum devices.