Towards the Dynamic Learning of an Experimental Entanglement Witness

We present a dynamic learning paradigm for "programming" a general quantum computer. A learning algorithm is used to find the control parameters for a coupled qubit system, such that the system at an initial time evolves to a state in which a given measurement corresponds to the desired operation. We apply the method to a system of coupled superconducting quantum interference devices (SQUIDs), and demonstrate learning of the XOR, XNOR, Toffoli, and Fredkin gates. Striking out for more interesting territory, we attempt learning of an entanglement witness for a two qubit system. Simulation shows a reasonably successful mapping of the entanglement at the initial time onto the correlation function at the final time for both pure and mixed states. For pure states this mapping requires knowledge of the phase relation between the parts; however, given that knowledge, this method can be used to measure the entanglement of an unknown state.