---
tags: QAOA, NISQ, Noise, Numerical
sort-date: 2024-03-06
description: 2024 APS March Meeting | 6 March 2024 08:36–08:48 CST | Minneapolis, Minnesota, United States of America
---
[[/index|Christopher K. Long]]<a href="https://orcid.org/0009-0001-3230-942X"><font color="#a6ce39"><i class='fa-brands fa-orcid'></i></font></a>,
Nikola Yanakiev<a href="https://orcid.org/0009-0008-1924-9005"><font color="#a6ce39"><i class='fa-brands fa-orcid'></i></font></a>,
Crispin H. W. Barnes<a href="https://orcid.org/0000-0001-7337-7245"><font color="#a6ce39"><i class='fa-brands fa-orcid'></i></font></a>,
Normann Mertig<a href="https://orcid.org/0000-0003-3025-7141"><font color="#a6ce39"><i class='fa-brands fa-orcid'></i></font></a>,
and David R. M. Arvidsson-Shukur<a href="https://orcid.org/0000-0002-0185-0352"><font color="#a6ce39"><i class='fa-brands fa-orcid'></i></font></a>

**Date & time:** 6 March 2024 08:36–08:48 CST

**Location:** Minneapolis, Minnesota, United States of America

**Conference:** [2024 APS March Meeting](https://meetings.aps.org/Meeting/MAR24/Content/4535) [@aps_mar24]

I presented work from Ref.&nbsp;[@PhysRevA.109.032420]. This was part II/II following [[Good and bad news for noisy variational quantum algorithms—Part I]].

Download slide deck: [[PowerPoints/Good and bad news for noisy variational quantum algorithms—Part II.pptx|PPTX]], [[PDFs/Good and bad news for noisy variational quantum algorithms—Part II.pdf|PDF]]

# Abstract

From Ref. [@aps_mar24_talk2]:

> The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems on noisy intermediate-scale quantum (NISQ) hardware. Making NISQ implementations of the QAOA resilient to noise requires short ansatz circuits with as few CNOT gates as possible. In this talk, we present Dynamic-ADAPT-QAOA. Our algorithm significantly reduces the circuit depth and the CNOT count of standard ADAPT-QAOA, a leading proposal for near-term implementations of the QAOA. Throughout our algorithm, the decision to apply CNOT-intensive operations is made dynamically, based on algorithmic benefits. Using density-matrix simulations, we benchmark the noise resilience of ADAPT-QAOA and Dynamic-ADAPT-QAOA. We compute the gate-error probability p below which these algorithms provide, on average, more accurate solutions than the classical, polynomial-time approximation algorithm by Goemans and Williamson. For small systems with 6–10 qubits, we show that p > 0.001 for Dynamic-ADAPT-QAOA. Compared to standard ADAPT-QAOA, this constitutes an order-of-magnitude improvement in noise resilience. This improvement should make Dynamic-ADAPT-QAOA viable for implementations on superconducting NISQ hardware, even in the absence of error mitigation.