Memory is a precious commodity across many different areas. I will present two ways in which quantum devices can lower memory requirements. The first application is the simulation of stochastic processes, i.e. processes that exhibit some randomness. We have experimentally realized quantum simulations of classical stochastic processes. Our simulators have lower memory requirements than the optimal classical simulators. The second type of quantum device I will discuss is a quantum autoencoder, which autonomously learns how to compress quantum data. We have developed and experimentally realized a photonic quantum autoencoder that is trained based on sets of quantum states.
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