$i$-QER: An Intelligent Approach towards Quantum Error Reduction

• URL: http://arxiv.org/abs/2110.06347v2
• Date: Sat, 2 Apr 2022 09:13:13 GMT
• Title: $i$-QER: An Intelligent Approach towards Quantum Error Reduction
• Authors: Saikat Basu and Amit Saha and Amlan Chakrabarti and Susmita Sur-Kolay
• Abstract summary: We introduce $i$-QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit. The $i$-QER predicts possible errors in a given quantum circuit using supervised learning models. It cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy.
• Score: 5.055934439032756
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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