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Entanglement is a key resource in many quantum information applications andachieving high values independently of the initial conditions is an importanttask. Here we address the problem of generating highly entangled states in adiscrete time quantum walk irrespective of the initial state using twodifferent approaches. First, we present and analyze a deterministic sequence ofcoin operators which produces high values of entanglement in a universal mannerfor a class of localized initial states. In a second approach, we directlyoptimize the sequence of coin operators using a reinforcement learningalgorithm. While the amount of entanglement produced by the deterministicsequence is fully independent of the initial states considered, the optimizedsequences achieve in general higher average values of entanglement that dohowever depend on the initial state parameters. Our proposed sequence andoptimization algorithm are especially useful in cases where the initial stateis not fully known or entanglement has to be generated in a universal mannerfor a range of initial states.

Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks