When could NISQ algorithms start to create value in discrete manufacturing ?

TL;DR

This paper explores the potential of NISQ-era quantum algorithms like QA, QAOA, and DQC to provide near-term advantages in discrete manufacturing, highlighting current evidence and future research directions.

Contribution

The paper assesses the current state and potential of NISQ algorithms for manufacturing applications, identifying promising approaches and areas needing further investigation.

Findings

QAOA shows potential but requires deeper circuits.

No conclusive evidence of quantum advantage for QA yet.

DQC with quantum feature maps are promising for nonlinear PDEs.

Abstract

Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.