Evolving Artificial Spin Ice for Robust Computation

Abstract

Artificial spin ice is a magnetic metamaterial showing particular promise as a novel substrate for unconventional computing. While simulations are invaluable for investigating new computational substrates, results must be robust to the noise and disorder of the physical world for device realisation. Here we investigate the computational robustness of artificial spin ice towards fabrication disorder. Using an evolutionary search, we explore different geometries of artificial spin ice for robust computation. We show that by neglecting to consider disorder in the search, we obtain geometries that suffer greatly when disorder is introduced. We then demonstrate that by explicitly including disorder as part of the evolutionary search process, we are able to discover novel geometries that are robust against disorder. We also find that these geometries perform well on new instances of disorder, and when they fail, we see signs of graceful degradation.