Scaling quantum approximate optimization on near-term hardware

The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale wi...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2022-07, Vol.12 (1), p.12388-12388, Article 12388
Main Authors: Lotshaw, Phillip C., Nguyen, Thien, Santana, Anthony, McCaskey, Alexander, Herrman, Rebekah, Ostrowski, James, Siopsis, George, Humble, Travis S.
Format: Article
Language:English
Subjects:
639/705/1042
639/766/259
639/766/483/481
Computational science
Computer applications
Computers
Humanities and Social Sciences
Information theory and computation
MATHEMATICS AND COMPUTING
multidisciplinary
Optimization
Quantum information
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale with problem size and complexity for realistic hardware implementations. Here, we quantify scaling of the expected resource requirements by synthesizing optimized circuits for hardware architectures with varying levels of connectivity. Assuming noisy gate operations, we estimate the number of measurements needed to sample the output of the idealized QAOA circuit with high probability. We show the number of measurements, and hence total time to solution, grows exponentially in problem size and problem graph degree as well as depth of the QAOA ansatz, gate infidelities, and inverse hardware graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-14767-w