  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 paper, we have
introduced $i$-QER, a scalable machine learning-based approach to evaluate
errors in a quantum circuit and helps to 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.

Quantum Physics