A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers

arXiv:2404.12892v1 Announce Type: cross
Abstract: Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting …

abstract arxiv computational computers cs.lg cs.se current development error errors google however ibm machine machine learning noise quantum quantum computers results software software development type