Adding controlled noise from a simulated time crystal improved fitting accuracy for two quantum neural network variants while degrading quantum reservoir computing, in small numerical tests.
Discrete Time Crystals
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abstract
Experimental advances have allowed for the exploration of nearly isolated quantum many-body systems whose coupling to an external bath is very weak. A particularly interesting class of such systems is those which do not thermalize under their own isolated quantum dynamics. In this review, we highlight the possibility for such systems to exhibit new non-equilibrium phases of matter. In particular, we focus on "discrete time crystals", which are many-body phases of matter characterized by a spontaneously broken discrete time translation symmetry. We give a definition of discrete time crystals from several points of view, emphasizing that they are a non-equilibrium phenomenon, which is stabilized by many-body interactions, with no analog in non-interacting systems. We explain the theory behind several proposed models of discrete time crystals, and compare a number of recent realizations, in different experimental contexts.
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The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Adding controlled noise from a simulated time crystal improved fitting accuracy for two quantum neural network variants while degrading quantum reservoir computing, in small numerical tests.