Statistical Complexity: Applications in Electronic Structure

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Discovering phase transitions with unsupervised learning. B 94 , Carrasquilla, J.

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Machine learning phases of matter. Broecker, P. Machine learning quantum phases of matter beyond the fermion sign problem. Machine learning phases of strongly correlated fermions. X 7 , Van Nieuwenburg, E. Learning phase transitions by confusion. Arsenault, L. Machine learning for many-body physics: the case of the anderson impurity model. B 90 , Kusne, A.

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    Data 1 , Kandala, A. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. The circuit is obtained by merging and operations from Fig. It applies the G operation to a range of values, instead of to a single value, by using an accumulator. The accumulator is guaranteed to be cleared after the final cnot targeting it drawn as a line merging into an ancilla qubit.

    This occurs because unless control is not set and the accumulator simply stays unset exactly one of the unary bits must have been set, and we targeted the accumulator with cnot s controlled by each of those bits in turn. This application is accomplished by performing a ranged operation as shown in Fig.

    Finite-sized example of the QROM database loading scheme used in our implementation of subprepare. The top part of the circuit performs unary iteration, as described in Sec.