Quantum artificial vision for defect detection in manufacturing
- URL: http://arxiv.org/abs/2208.04988v2
- Date: Fri, 5 Jan 2024 12:01:04 GMT
- Title: Quantum artificial vision for defect detection in manufacturing
- Authors: Daniel Guijo, Victor Onofre, Gianni Del Bimbo, Samuel Mugel, Daniel
Estepa, Xabier De Carlos, Ana Adell, Aizea Lojo, Josu Bilbao, Roman Orus
- Abstract summary: We consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices.
We benchmark them for a real problem against their classical counterparts.
This is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we consider several algorithms for quantum computer vision
using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a
real problem against their classical counterparts. Specifically, we consider
two approaches: a quantum Support Vector Machine (QSVM) on a universal
gate-based quantum computer, and QBoost on a quantum annealer. The quantum
vision systems are benchmarked for an unbalanced dataset of images where the
aim is to detect defects in manufactured car pieces. We see that the quantum
algorithms outperform their classical counterparts in several ways, with QBoost
allowing for larger problems to be analyzed with present-day quantum annealers.
Data preprocessing, including dimensionality reduction and contrast
enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To
the best of our knowledge, this is the first implementation of quantum computer
vision systems for a problem of industrial relevance in a manufacturing
production line.
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