<i>i</i> -QER: An Intelligent Approach Towards Quantum Error Reduction

Quantum computing has become a promising approach because of its capability to solve certain problems, exponentially faster than classical computers. A n -qubit quantum system is capable providing 2 computational space algorithm. However, computers are prone errors. circuits that can reliably run on today’s Noisy Intermediate-Scale (NISQ) devices not only limited by their qubit counts but also noisy gate operations. In this article, we have introduced i -QER, scalable machine learning-based evaluate errors in circuit and reduce these without using any additional resources. The -QER predicts possible given supervised learning models. If the predicted error above pre-specified threshold, it cuts large into two smaller sub-circuits an error-influenced fragmentation strategy for first time best our knowledge. proposed process iterated until reaches below threshold each sub-circuit. then executed device. Classical reconstruction outputs obtained from generate output complete circuit. Thus, provides control over hybrid approach, which combination tool available at https://github.com/SaikatBasu90/i-QER .