i-QER

Machine-learning system for predicting and reducing errors in quantum circuits on NISQ hardware without additional quantum resources. A supervised-learning model estimates a circuit's error; when it exceeds a threshold, an error-influenced fragmentation strategy splits the circuit into smaller sub-circuits, repeating until each falls below the threshold, after which the sub-circuits run on quantum devices and classical post-processing reconstructs the full output. This hybrid quantum-classical approach keeps error reduction under classical control while improving the reliability of larger circuits.

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SaikatBasu90/i-QER

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Machine Learning Based Error Mitigation

This tool considers a handfull of quantum circuit execution data from any specific quantum hardware. Here the data of IBMQ_16_melbourne is considered. The idea is to minimize the number of fragmentation of a large quantum circuit with respect to the error predicted. This tool also be used for Error Influenced Binary Fragmentation Algorithm, thus providing a Classical on a hybrid quantum-classical computing system.

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