Quantum Machine Learning with HQC Architectures using non-Classically\n Simulable Feature Maps

2021/03/21 by Syed Farhan Ahmad, Ahmad, Syed Farhan, Raghav Rawat +3
Computer Science · #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2103.11381

openalex publication_date 2021/03/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ\nQuantum Computers for solving Quantum Machine Learning problems. The quantum\nadvantage comes into picture due to the exponential speedup offered over\nclassical computing. One of the major challenges in implementing such\nalgorithms is the choice of quantum embeddings and the use of a functionally\ncorrect quantum variational circuit. In this paper, we present an application\nof QSVM (Quantum Support Vector Machines) to predict if a person will require\nmental health treatment in the tech world in the future using the dataset from\nOSMI Mental Health Tech Surveys. We achieve this with non-classically simulable\nfeature maps and prove that NISQ HQC Architectures for Quantum Machine Learning\ncan be used alternatively to create good performance models in near-term\nreal-world applications.\n

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