Quantum neural network filters for signal processing have received a lot of interest in the recent past. The implementations of these filters had a number of design parameters that led to numerical inefficiencies. At ...Quantum neural network filters for signal processing have received a lot of interest in the recent past. The implementations of these filters had a number of design parameters that led to numerical inefficiencies. At the same time the solution procedures employed were explicit in that the evolution of the time-varying functions had to be controlled. This often led to numerical instabilities. This paper outlines a procedure for improving the stability, numerical efficiency, and the accuracy of quantum neural network filters. Two examples are used to illustrate the principles employed.展开更多
文摘Quantum neural network filters for signal processing have received a lot of interest in the recent past. The implementations of these filters had a number of design parameters that led to numerical inefficiencies. At the same time the solution procedures employed were explicit in that the evolution of the time-varying functions had to be controlled. This often led to numerical instabilities. This paper outlines a procedure for improving the stability, numerical efficiency, and the accuracy of quantum neural network filters. Two examples are used to illustrate the principles employed.
