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

TL;DR

The paper introduces i-QER, a machine learning-based method to evaluate and reduce errors in quantum circuits by fragmenting large circuits into smaller ones, enabling more reliable execution on noisy quantum devices without extra quantum resources.

Contribution

i-QER is the first scalable approach that predicts errors and adaptively fragments quantum circuits to improve error rates without additional quantum overhead.

Findings

Effective error prediction using supervised learning models.

Successful fragmentation reduces circuit errors below thresholds.

Enables classical-quantum hybrid execution for noisy quantum devices.

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.