RCGP: An Automatic Synthesis Framework for Reversible Quantum-Flux-Parametron Logic Circuits based on Efficient Cartesian Genetic Programming
Abstract
Reversible computing has gained increasing attention as a prospective solution for energy dissipation, particularly in quantum computing. As the first practical reversible logic gate using adiabatic superconducting devices, the reversible quantum-flux-parametron (RQFP) has been experimentally demonstrated in logical and physical reversibility. However, the circuit design of RQFP logic poses enormous challenges due to its distinctive logic function and structure. Furthermore, the circuit scale severely restricts the applicability of the existing exact logic synthesis method for RQFP logic. Therefore, this paper proposes RCGP, an automatic synthesis framework based on efficient Cartesian genetic programming, to generate large RQFP logic circuits. RCGP considers the characteristics of RQFP logic circuits to minimize the number of gates and garbage outputs. Meanwhile, RCGP combines circuit simulation with formal verification to assess the functional equivalence between the parent and its offspring. Experimental results on reversible logic benchmarks demonstrate the effectiveness of RCGP.
