How to use the qutip.tensor function in qutip

def test_tensor_product():
    H = LocalSpace(hs_name(), dimension=5)
    a = Create(hs=H).adjoint()
    H2 = LocalSpace(hs_name(), basis=("e", "g", "h"))
    sigma = LocalSigma('g', 'e', hs=H2)
    assert convert_to_qutip(sigma * a) == \
                        qutip.tensor(convert_to_qutip(a),
                                    convert_to_qutip(sigma))

def test_gate_objectives_shape_error():
    """Test that trying to construct gate objectives with a gate whose shape
    mismatches the basis throws an exception"""
    basis = [qutip.ket([0]), qutip.ket([1])]
    gate = qutip.tensor(qutip.operators.sigmay(), qutip.identity(2))
    H = [
        qutip.operators.sigmaz(),
        [qutip.operators.sigmax(), lambda t, args: 1.0],
    ]
    with pytest.raises(ValueError) as exc_info:
        krotov.objectives.gate_objectives(basis, gate, H)
    assert "same dimension as the number of basis" in str(exc_info.value)

def test_liouvillian():
    """Test conversion of Hamiltonian/Lindblad operators into a Liouvillian"""
    H = [
        tensor(sigmaz(), identity(2)) + tensor(identity(2), sigmaz()),
        [tensor(sigmax(), identity(2)), lambda t, args: 1.0],
        [tensor(identity(2), sigmax()), lambda t, args: 1.0],
    ]
    c_ops = [tensor(sigmam(), identity(2)), tensor(identity(2), sigmam())]

    assert (
        krotov.objectives.liouvillian(H[0], c_ops)
        - qutip.liouvillian(H[0], c_ops)
    ).norm('max') < 1e-15

    L = krotov.objectives.liouvillian(H, c_ops)
    assert isinstance(L, list)
    assert len(L) == 3
    assert (L[0] - qutip.liouvillian(H[0], c_ops)).norm('max') < 1e-15
    assert (L[1][0] - qutip.liouvillian(H[1][0])).norm('max') < 1e-15
    assert (L[2][0] - qutip.liouvillian(H[2][0])).norm('max') < 1e-15
    assert L[1][1] is H[1][1]
    assert L[2][1] is H[2][1]

    with pytest.raises(ValueError):

def __init__(self, capacity=3):         
        self.capacity = capacity         
        self.available_qubits = [                                        # <5>
            SimulatedQubit(self, idx)         
            for idx in range(capacity)         
        ]         
        self.register_state = qt.tensor(                                 # <6>
            *[         
                qt.basis(2, 0)         
                for _ in range(capacity)         
            ]

def _localop(op, l, k):
    """
    Create a local operator on the l'th system by tensoring
    with identity operators on all the other k-1 systems
    """
    if l < 1 or l > k:
        raise IndexError('index l out of range')
    h = op
    I = qt.qeye(op.shape[0])
    I.dims = op.dims
    for i in range(1, l):
        h = qt.tensor(I, h)
    for i in range(l+1, k+1):
        h = qt.tensor(h, I)
    return h

def _convert_local_operator_to_qutip(expr, full_space, mapping):
    """Convert a LocalOperator instance to qutip"""
    n = full_space.dimension
    if full_space != expr.space:
        all_spaces = full_space.local_factors
        own_space_index = all_spaces.index(expr.space)
        return qutip.tensor(
            *([qutip.qeye(s.dimension)
               for s in all_spaces[:own_space_index]] +
              [convert_to_qutip(expr, expr.space, mapping=mapping), ] +
              [qutip.qeye(s.dimension)
               for s in all_spaces[own_space_index + 1:]])
        )
    if isinstance(expr, Create):
        return qutip.create(n)
    elif isinstance(expr, Jz):
        return qutip.jmat((expr.space.dimension-1)/2., "z")
    elif isinstance(expr, Jplus):
        return qutip.jmat((expr.space.dimension-1)/2., "+")
    elif isinstance(expr, Jminus):
        return qutip.jmat((expr.space.dimension-1)/2., "-")
    elif isinstance(expr, Destroy):
        return qutip.destroy(n)

N = int(N)
    sx = [0 for i in range(N)]
    sy = [0 for i in range(N)]
    sz = [0 for i in range(N)]
    sp = [0 for i in range(N)]
    sm = [0 for i in range(N)]

    sx[0] = 0.5 * sigmax()
    sy[0] = 0.5 * sigmay()
    sz[0] = 0.5 * sigmaz()
    sp[0] = sigmap()
    sm[0] = sigmam()

    # 2. Place operators in total Hilbert space
    for k in range(N - 1):
        sx[0] = tensor(sx[0], identity(2))
        sy[0] = tensor(sy[0], identity(2))
        sz[0] = tensor(sz[0], identity(2))
        sp[0] = tensor(sp[0], identity(2))
        sm[0] = tensor(sm[0], identity(2))

    # 3. Cyclic sequence to create all N operators
    a = [i for i in range(N)]
    b = [[a[i - i2] for i in range(N)] for i2 in range(N)]

    # 4. Create N operators
    for i in range(1, N):
        sx[i] = sx[0].permute(b[i])
        sy[i] = sy[0].permute(b[i])
        sz[i] = sz[0].permute(b[i])
        sp[i] = sp[0].permute(b[i])
        sm[i] = sm[0].permute(b[i])

Returns
        ----------
        Qobj(Hamil) : qutip.Qobj
            oper type Quantum object representing the lattice Hamiltonian.
        """
        D = qeye(self.num_cell)
        T = np.diag(np.zeros(self.num_cell-1)+1, 1)
        Tdag = np.diag(np.zeros(self.num_cell-1)+1, -1)

        if self.period_bnd_cond_x == 1 and self.num_cell > 2:
            Tdag[0][self.num_cell-1] = 1
            T[self.num_cell-1][0] = 1
        T = Qobj(T)
        Tdag = Qobj(Tdag)
        Hamil = tensor(D, self._H_intra) + tensor(
                T, self._H_inter) + tensor(Tdag, self._H_inter.dag())
        dim_H = [self.lattice_tensor_config, self.lattice_tensor_config]
        return Qobj(Hamil, dims=dim_H)

def _pseudo_inverse_dense(L, rhoss, w=None, **pseudo_args):
    """
    Internal function for computing the pseudo inverse of an Liouvillian using
    dense matrix methods. See pseudo_inverse for details.
    """
    rho_vec = np.transpose(mat2vec(rhoss.full()))

    tr_mat = tensor([identity(n) for n in L.dims[0][0]])
    tr_vec = np.transpose(mat2vec(tr_mat.full()))
    N = np.prod(L.dims[0][0])
    I = np.identity(N * N)
    P = np.kron(np.transpose(rho_vec), tr_vec)
    Q = I - P

    if w is None:
        L = L
    else:
        L = 1.0j*w*spre(tr_mat)+L

    if pseudo_args['method'] == 'direct':
        try:
            LIQ = np.linalg.solve(L.full(), Q)
        except:
            LIQ = np.linalg.lstsq(L.full(), Q)[0]