A Step Forward in the Design of Nano-Scale Circuits using Machine Intelligence

One of the promising ideas to improve over CMOS constrains in the nano-scales is Quantum Cellular Automata (QCA). So far, a variety of logic circuits are designed based on QCA where usually the majority and the inverter gates are the basic building blocks from which more complicated circuits are developed. In this paper, first we propose an approach to minimize the number of the majority and inverter gates in a given circuit with multiple inputs/outputs (MIMO). In our proposal, which is based on Cartesian Genetic Programming (CGP), a QCA circuit is coded as a series of integer numbers that constitutes a genotype for CGP. Applying CGP operators then, outputs the optimum phenotype including the number and the type of gates along with their interconnections. As for the verification of this approach, we apply it to 27 logic circuits and the results are reported, which show better solutions (in majority of cases) compared to the competing approaches. In addition to a fewer number of gates, our approach may provide a way to design QCA circuits with less power dissipation and/or less occupied areas.