  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 specific noise types or specific quantum
circuits. This paper proposes a practical ML-based approach, called Q-LEAR,
with a novel feature set, to mitigate noise errors in quantum software outputs.
We evaluated Q-LEAR on eight quantum computers and their corresponding noisy
simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based
approach taken as baseline. Results show that, compared to the baseline, Q-LEAR
achieved a 25% average improvement in error mitigation on both real quantum
computers and simulators. We also discuss the implications and practicality of
Q-LEAR, which, we believe, is valuable for practitioners.
