Applied Nanophotonics, Liquid Crystals and Biomedical Optics

Many problems in mathematics, statistical mechanics, and computer science are hard to tackle by conventional computational methods, but can be reduced to the combinatorial problem of finding the ground state of the Ising model. However, the computational cost associated with a combinatorial optimization problem increases exponentially with the number of variables. This explains growing research interest towards physical and artificial spin-networks (schematically shown in Fig. 1 (a) ) that evolve according to an Ising Hamiltonian, which provides the combinatorial solution through the ground state observed in the experiment [1]. The implementation of analog autonomous spin-networks is currently considered a highly attractive prospect due to a potentially significant increase in energy efficiency and processing speed.