Markov Inequality as a Tool for Linear-Scaling Estimation of Local Observables

Abstract

We introduce a linear-scaling stochastic method to compute real-space maps of any positive local spectral operator in a tight-binding model. By employing positive-definite estimators, the sampling error at each site can be rigorously bounded relative to the mean via the Markov inequality, overcoming the lack of self-averaging and enabling accurate estimates even under strong spatial fluctuations. The approach extends to non-diagonal observables, such as local currents, through local unitary transformations and its effectiveness is showcased by benchmark calculations in the disordered two-dimensional (2D) $\pi$-flux model, where the LDoS and steady-state current maps are computed. This method will enable simulations of disorder-driven mesoscopic phenomena in realistically large lattices and accelerate real-space self-consistent mean-field calculations.