Commit 24bed862 authored by kerry-he's avatar kerry-he
Browse files

Fixed QRE computation

parent 22839091

picos/expressions/exp_quantentr.py

+8 −8
Original line number Diff line number Diff line
@@ -166,10 +166,9 @@ class QuantumEntropy(Expression): 
    def _get_value(self):

        X = cvx2np(self._X._get_value())

        eigvalsX, eigvecsX = numpy.linalg.eigh(X)

        eigvecsX = eigvecsX[:, eigvalsX > 1e-12]

        eigvalsX = eigvalsX[eigvalsX > 1e-12]



        if self._Y is None:

            eigvalsX = eigvalsX[eigvalsX > 1e-12]

            s = -numpy.sum(eigvalsX * numpy.log(eigvalsX))

        else:

            Y = eigvecsX.conj().T @ cvx2np(self._Y._get_value()) @ eigvecsX
@@ -177,8 +176,9 @@ class QuantumEntropy(Expression): 
            Dy, Uy = numpy.linalg.eigh(Y)

            logY = Uy @ numpy.diag(numpy.log(Dy)) @ Uy.conj().T



            s = -numpy.sum(eigvalsX * numpy.log(eigvalsX))

            s += numpy.sum(numpy.diag(eigvalsX) * logY.conj()).real

            s = numpy.sum(numpy.diag(eigvalsX) * logY.conj()).real

            eigvalsX = eigvalsX[eigvalsX > 1e-12]

            s -= numpy.sum(eigvalsX * numpy.log(eigvalsX))



        return cvxopt.matrix(s)
@@ -405,10 +405,9 @@ class NegativeQuantumEntropy(Expression): 
    def _get_value(self):

        X = cvx2np(self._X._get_value())

        eigvalsX, eigvecsX = numpy.linalg.eigh(X)

        eigvecsX = eigvecsX[:, eigvalsX > 1e-12]

        eigvalsX = eigvalsX[eigvalsX > 1e-12]



        if self._Y is None:

            eigvalsX = eigvalsX[eigvalsX > 1e-12]

            s = numpy.sum(eigvalsX * numpy.log(eigvalsX))

        else:

            Y = eigvecsX.conj().T @ cvx2np(self._Y._get_value()) @ eigvecsX
@@ -416,8 +415,9 @@ class NegativeQuantumEntropy(Expression): 
            Dy, Uy = numpy.linalg.eigh(Y)

            logY = Uy @ numpy.diag(numpy.log(Dy)) @ Uy.conj().T



            s = numpy.sum(eigvalsX * numpy.log(eigvalsX))

            s -= numpy.sum(numpy.diag(eigvalsX) * logY.conj()).real

            s = -numpy.sum(numpy.diag(eigvalsX) * logY.conj()).real

            eigvalsX = eigvalsX[eigvalsX > 1e-12]

            s += numpy.sum(eigvalsX * numpy.log(eigvalsX))



        return cvxopt.matrix(s)