Classical Simulation

Classical simulation of quantum computations aims to reproduce the results of quantum algorithms using classical computers, thereby assessing the true quantum advantage of proposed methods. Current research focuses on determining the classical simulability of various quantum machine learning models, particularly variational quantum circuits and quantum convolutional neural networks, often analyzing their performance on benchmark datasets. This work is crucial for evaluating the practical utility of near-term quantum devices and identifying scenarios where quantum computers offer a demonstrable speedup over classical approaches, guiding the development of truly quantum algorithms. Furthermore, efficient classical simulation techniques are vital for algorithm design, verification, and error mitigation in the noisy intermediate-scale quantum era.