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