Evaluating the Impact of Noise on Variational Quantum Circuits in NISQ Era Devices
Bikram Khanal, Pablo Rivas
Abstract
The limited supply of qubits and significant quantum noise impose limitations on the capability of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era. NISQ devices have a variety of applications, such as Variational Quantum Circuit (VQC), which provides answers to difficult optimization and machine learning problems. This paper presents a thorough investigation of quantum variational classification in the NISQ context, with a focus on comprehending noise's impact on various feature maps and VQCs. We evaluate the effectiveness of quantum classifiers using a variety of datasets, ranging from straightforward binary classification problems to more complex tasks. Our results reveal the critical role that feature maps and variational circuit selection play in mitigating the effect of noise, identifying specific quantum circuit designs that exhibit robustness even in noisy situations. In order to highlight the potential of quantum machine learning in solving complex problems within in the NISQ setting, this study emphasizes the delicate interaction between feature map selection, variational circuit design, dataset complexity, and quantum noise.