Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices

arXiv:2404.02177v1 Announce Type: cross
Abstract: As medium-scale quantum computers progress, the application of quantum algorithms across diverse fields like simulating physical systems, chemistry, optimization, and cryptography becomes more prevalent. However, these quantum computers, known as Noisy Intermediate Scale Quantum (NISQ), are susceptible to noise, prompting the search for applications that can capitalize on quantum advantage without extensive error correction procedures. Since, Machine Learning (ML), particularly Deep Learning (DL), faces challenges due to resource-intensive training and algorithmic opacity. Therefore, this study …

abstract algorithms application applications arxiv chemistry computer computers computer vision cryptography cs.lg devices diverse fields however insights intermediate machine machine learning medium nisq noise optimization progress prompting quant-ph quantum quantum computers scale systems type vision