A Modified Depolarization Approach for Efficient Quantum Machine Learning

arXiv:2404.07330v1 Announce Type: cross
Abstract: Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, errors, and decoherence that complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system's noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. …

abstract applications arxiv challenges computing cryptography cs.lg errors intermediate machine machine learning nisq noise optimization progress quant-ph quantum quantum computing scale simulation standard systems the simulation tool type