Noise tailoring for Robust Amplitude Estimation

TL;DR

This paper introduces a noise-tailoring technique for robust amplitude estimation (RAE) on NISQ devices, improving its bias and precision by effectively modeling device noise, thus enhancing the feasibility of quantum advantage with noisy quantum computers.

Contribution

The paper proposes a novel noise-tailoring approach that enables RAE to better handle realistic noise, improving its performance on NISQ devices without explicit noise modeling.

Findings

Noise-tailored RAE improves bias and precision in simulations.

Algorithm demonstrates advantage over standard methods on IBM quantum hardware.

Effective noise modeling enhances RAE's robustness on NISQ devices.

Abstract

A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (NISQ) computers and, perhaps, early fault tolerant (EFT) quantum computers. This motivates the development of many near-term quantum algorithms including robust amplitude estimation (RAE), which is a quantum-enhanced algorithm for estimating expectation values. One obstacle to using RAE has been a paucity of ways of getting realistic error models incorporated into this algorithm. So far the impact of device noise on RAE is incorporated into one of its subroutines as an exponential decay model, which is unrealistic for NISQ devices and, maybe, for EFT devices; this hinders the performance of RAE. Rather than trying to explicitly model realistic noise effects, which may be infeasible, we circumvent this obstacle by tailoring device noise to generate an effective noise model, whose impact on RAE closely resembles that of the exponential decay model. Using noisy simulations, we show that our noise-tailored RAE algorithm is able to regain improvements in both bias and precision that are expected for RAE. Additionally, on IBM's quantum computer our algorithm demonstrates advantage over the standard estimation technique in reducing bias. Thus, our work extends the feasibility of RAE on NISQ computers, consequently bringing us one step closer towards achieving quantum advantage using these devices.