2024/04/01 by Purnachandra Mandadapu, Mandadapu, Purnachandra
Computer Science · Engineering · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2404.02177
openalex publication_date 2024/04/01 · openalex created_date 2024/04/05 · openalex updated_date 2026/07/28
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 explores the intersection of quantum computing and ML, focusing on computer vision tasks. Specifically, it evaluates the effectiveness of hybrid quantum-classical algorithms, such as the data re-uploading scheme and the patch Generative Adversarial Networks (GAN) model, on small-scale quantum devices. Through practical implementation and testing, the study reveals comparable or superior performance of these algorithms compared to classical counterparts, highlighting the potential of leveraging quantum algorithms in ML tasks.