Optimizing Quantum Algorithms for Noise-Resilient Quantum Computing in Near-Term Quantum Devices

Abstract

Quantum computing has the potential to outperform classical computing in solving complex problems, but current Noisy Intermediate-Scale Quantum (NISQ) devices are limited by noise, decoherence, and gate errors. This article explores various strategies for optimizing quantum algorithms to make them resilient to noise in near-term quantum systems. These approaches include error mitigation techniques, hybrid classical-quantum algorithms, error-aware algorithm design, and noise-adaptive quantum machine learning. By focusing on noise resilience, these strategies enhance the capabilities of NISQ devices, paving the way for practical quantum computing applications while bridging the gap until fully fault-tolerant quantum systems become available.