Wave Computing based on Dynamical Networks: Applications in Optimization Problems

We develop a computing framework that leverages wave propagation within an interconnected network, where nodes and edges possess wave manipulation capabilities, such as frequency mixing or time delay. This computing paradigm can not only achieve intrinsic parallelism like existing works by the exploration of an exponential number of possibilities simultaneously with very small number of hardware units, but also extend this unique characteristic to a multidimensional space including spatial, temporal and frequency domains, making it particularly effective for addressing NP-hard problems. The proposed architecture has been validated through SPICE simulations, demonstrating its potential capability in solving several NP-hard problems, such as the Number Partitioning Problem, the 0/1 Knapsack Problem, and the Traveling Salesman Problem.