  Noise in quantum information processing poses a significant obstacle to
achieving precise results. Quantum error mitigation techniques are crucial for
improving the accuracy of experimental expectation values in this process. In
the experiments, it is commonly observed that some measured events violate
certain principles, such as symmetry constraints. These events can be
considered junk information and should be discarded in a post-selection
process. In this work, we introduce a quantum error mitigation method named
Self-Trained Quantum Noise Filter (SQNF), which leverages the junk information
to differentiate errors from the experimental population distributions, thereby
aiming to approximate the error-free distribution. Our numerical results
demonstrate that the proposed method can significantly reduce the infidelity of
population distributions compared to the traditional post-selection method.
Notably, the infidelity reduction is achieved without additional experimental
resource consumption. Our method is scalable and applicable to multi-qubit
computing systems.
