Reinforcement-learning generation of four-qubit entangled states

We have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states four qubits. This way, the is able generate representative for some of 49 true SLOCC classes four-qubit entanglement states. In particular, it possible reach at least one class each nine families. The quantum circuits synthesized by may be useful experimental realization these important and draw conclusions about intrinsic properties our universe. introduce a graphical tool called state-link graph (SLG) represent construction quality matrix (Q-matrix) used build given objective state belonging corresponding class. allows us discover necessary connections between specific features role certain gates, which needs include in gate set actions. found are optimal respect gate-set chosen. These SLGs make simple, intuitive, resource automated low number