Linear Order Matrix Inversion Method with Help from Quantum Searching Algorithm

Abstract

Presented here is a matrix inversion method utilizing quantum searching algorithm. In this method, huge Hilbert space as a whole spanned by myriad of eigen states is searched and evaluated efficiently by sequential reduction in dimension one by one. Total iteration steps required for search are proportional to the number of unknown variables. Our method could solve very large linear equations with sufficiently high probability faster than any existing classical algorithms, which roughly depends on the cube of unknown variables.