  Total least squares (TLS) methods have been widely used in data fitting.
Compared with the least squares method, for TLS problem we takes into account
not only the observation errors, but also the errors in the measurement matrix.
This is more realistic in practical applications. For the large-scale discrete
ill-posed problem $Ax \approx b$, we introduce the quantum-inspired techniques
to approximate the truncated total least squares (TTLS) solution. We analyze
the accuracy of the quantum-inspired truncated total least squares algorithm
and perform numerical experiments to demonstrate the efficiency of our method.
