Exemple #1
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def test_prefactor_vacuum():
    """test the correct prefactor of 0.5 is calculated for a vacuum state"""
    Q = np.identity(2)
    beta = np.zeros([2])

    res = prefactor(Means(beta), Covmat(Q))
    ex = 1
    assert np.allclose(res, ex)
Exemple #2
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def test_gen_Qmat_from_graph():
    """ Test the gen_Qmat_from_graph for the analytically solvable case of a single mode"""
    A = np.array([[10.0]])
    n_mean = 1.0
    cov = Covmat(gen_Qmat_from_graph(A, n_mean))
    r = np.arcsinh(np.sqrt(n_mean))
    cov_e = np.diag([(np.exp(2 * r)), (np.exp(-2 * r))])
    assert np.allclose(cov, cov_e)
Exemple #3
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def test_density_matrix_element_no_disp(t):
    """Test density matrix elements for a state with no displacement"""
    beta = Beta(np.zeros([6]))
    Q = Qmat(V)

    el = t[0]
    ex = t[1]
    res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
    assert np.allclose(ex, res)
Exemple #4
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def test_prefactor_TMS():
    """test the correct prefactor of 0.5 is calculated for a TMS state"""
    q = np.fliplr(np.diag([2.0] * 4))
    np.fill_diagonal(q, np.sqrt(2))
    Q = np.fliplr(q)

    beta = np.zeros([4])

    res = prefactor(Means(beta), Covmat(Q))
    ex = 0.5
    assert np.allclose(res, ex)
Exemple #5
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def test_density_matrix_element_vacuum():
    """Test density matrix elements for the vacuum"""
    Q = np.identity(2)
    beta = np.zeros([2])

    el = [[0], [0]]
    ex = 1
    res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
    assert np.allclose(ex, res)

    el = [[1], [1]]
    #    res = density_matrix_element(beta, A, Q, el[0], el[1])
    res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])

    assert np.allclose(0, res)

    el = [[1], [0]]
    #    res = density_matrix_element(beta, A, Q, el[0], el[1])
    res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])

    assert np.allclose(0, res)
Exemple #6
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def test_prefactor_with_displacement():
    """test the correct prefactor of 0.5 is calculated for a TMS state"""
    q = np.fliplr(np.diag([2.0] * 4))
    np.fill_diagonal(q, np.sqrt(2))
    Q = np.fliplr(q)
    Qinv = np.linalg.inv(Q)

    vect = 1.2 * np.ones([2]) + 1j * np.ones(2)
    beta = np.concatenate([vect, vect.conj()])

    res = prefactor(Means(beta), Covmat(Q), hbar=2)
    ex = np.exp(-0.5 * beta @ Qinv @ beta.conj()) / np.sqrt(np.linalg.det(Q))
    assert np.allclose(res, ex)
Exemple #7
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def test_Covmat():
    """ Test the Covmat function by checking that its inverse function is Qmat """
    n = 1
    B = np.random.rand(n, n) + 1j * np.random.rand(n, n)
    B = B + B.T
    sc = find_scaling_adjacency_matrix(B, 1)
    idm = np.identity(2 * n)
    X = Xmat(n)
    Bsc = sc * B
    A = np.block([[Bsc, 0 * Bsc], [0 * Bsc, Bsc.conj()]])
    Q = np.linalg.inv(idm - X @ A)
    cov = Covmat(Q)
    Qrec = Qmat(cov)
    assert np.allclose(Q, Qrec)