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

arXiv:2103.11381v2 Announce Type: replace-cross
Abstract: Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ Quantum Computers for solving Quantum Machine Learning problems. The quantum advantage comes into picture due to the exponential speedup offered over classical computing. One of the major challenges in implementing such algorithms is the choice of quantum embeddings and the use of a functionally correct quantum variational circuit. In this paper, we present an application of QSVM (Quantum Support Vector Machines) to predict if a person …

abstract algorithms architectures arxiv challenges computers computing cs.lg feature hybrid machine machine learning major maps near nisq quant-ph quantum quantum advantage quantum computers type