Low depth amplitude estimation on a trapped ion quantum computer

TL;DR

This paper demonstrates low-depth amplitude estimation on a trapped ion quantum computer, achieving notable accuracy improvements and analyzing noise-resilient algorithms for near-term quantum hardware.

Contribution

It presents the first experimental implementation of low-depth amplitude estimation algorithms on a trapped ion quantum computer, including novel noise-aware methods.

Findings

MLE-based approach achieved mean error of ~0.01 with circuits over 60 depth.

CRT approach provided accurate estimates but was less noise-robust.

Noise-aware algorithms further improved estimation accuracy.

Abstract

Amplitude estimation is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problems, including Monte Carlo methods. The main drawback from the perspective of near term hardware implementations is that the amplitude estimation algorithm requires very deep quantum circuits. Recent works have succeeded in somewhat reducing the necessary resources for such algorithms, by trading off some of the speedup for lower depth circuits, but high quality qubits are still needed for demonstrating such algorithms. Here, we report the results of an experimental demonstration of amplitude estimation on a state-of-the-art trapped ion quantum computer. The amplitude estimation algorithms were used to estimate the inner product of randomly chosen four-dimensional unit vectors, and were based on the maximum likelihood estimation (MLE) and the Chinese remainder theorem (CRT) techniques. Significant improvements in accuracy were observed for the MLE based approach when deeper quantum circuits were taken into account, including circuits with more than ninety two-qubit gates and depth sixty, achieving a mean additive estimation error on the order of $10^{-2}$. The CRT based approach was found to provide accurate estimates for many of the data points but was less robust against noise on average. Last, we analyze two more amplitude estimation algorithms that take into account the specifics of the hardware noise to further improve the results.