Dynamic learning of pairwise and three-way entanglement

In previous work, we have developed a dynamic learning paradigm for “programming” a general quantum computer. A learning algorithm is used to find a set of parameters for a coupled qubit system such that the system at an initial time evolves at the final time to a state in which a given measurement results in the desired calculation value. This can be thought of as a quantum neural network (QNN). Here, we apply our method to a system of three qubits, and demonstrate training the quantum computer to estimate both pairwise and three-way entanglement of the initial state.