Exemplo n.º 1
0
def tensor_contract(qobj, *pairs):
    """Contracts a qobj along one or more index pairs.
    Note that this uses dense representations and thus
    should *not* be used for very large Qobjs.

    Parameters
    ----------

    pairs : tuple
        One or more tuples ``(i, j)`` indicating that the
        ``i`` and ``j`` dimensions of the original qobj
        should be contracted.

    Returns
    -------

    cqobj : Qobj
        The original Qobj with all named index pairs contracted
        away.

    """
    # Record and label the original dims.
    dims = qobj.dims
    dims_idxs = enumerate_flat(dims)
    tensor_dims = dims_to_tensor_shape(dims)

    # Convert to dense first, since sparse won't support the reshaping we need.
    qtens = qobj.data.toarray()

    # Reshape by the flattened dims.
    qtens = qtens.reshape(tensor_dims)

    # Contract out the indices from the flattened object.
    # Note that we need to feed pairs through dims_idxs_to_tensor_idxs
    # to ensure that we are contracting the right indices.
    qtens = _tensor_contract_dense(qtens,
                                   *dims_idxs_to_tensor_idxs(dims, pairs))

    # Remove the contracted indexes from dims so we know how to
    # reshape back.
    # This concerns dims, and not the tensor indices, so we need
    # to make sure to use the original dims indices and not the ones
    # generated by dims_to_* functions.
    contracted_idxs = deep_remove(dims_idxs, *flatten(list(map(list, pairs))))
    contracted_dims = unflatten(flatten(dims), contracted_idxs)

    # We don't need to check for tensor idxs versus dims idxs here,
    # as column- versus row-stacking will never move an index for the
    # vectorized operator spaces all the way from the left to the right.
    l_mtx_dims, r_mtx_dims = map(np.product, map(flatten, contracted_dims))

    # Reshape back into a 2D matrix.
    qmtx = qtens.reshape((l_mtx_dims, r_mtx_dims))

    # Return back as a qobj.
    return Qobj(qmtx, dims=contracted_dims, superrep=qobj.superrep)
Exemplo n.º 2
0
def tensor_contract(qobj, *pairs):
    """Contracts a qobj along one or more index pairs.
    Note that this uses dense representations and thus
    should *not* be used for very large Qobjs.

    Parameters
    ----------

    pairs : tuple
        One or more tuples ``(i, j)`` indicating that the
        ``i`` and ``j`` dimensions of the original qobj
        should be contracted.

    Returns
    -------

    cqobj : Qobj
        The original Qobj with all named index pairs contracted
        away.

    """
    # Record and label the original dims.
    dims = qobj.dims
    dims_idxs = enumerate_flat(dims)
    tensor_dims = dims_to_tensor_shape(dims)

    # Convert to dense first, since sparse won't support the reshaping we need.
    qtens = qobj.data.toarray()

    # Reshape by the flattened dims.
    qtens = qtens.reshape(tensor_dims)

    # Contract out the indices from the flattened object.
    # Note that we need to feed pairs through dims_idxs_to_tensor_idxs
    # to ensure that we are contracting the right indices.
    qtens = _tensor_contract_dense(qtens, *dims_idxs_to_tensor_idxs(dims, pairs))

    # Remove the contracted indexes from dims so we know how to
    # reshape back.
    # This concerns dims, and not the tensor indices, so we need
    # to make sure to use the original dims indices and not the ones
    # generated by dims_to_* functions.
    contracted_idxs = deep_remove(dims_idxs, *flatten(list(map(list, pairs))))
    contracted_dims = unflatten(flatten(dims), contracted_idxs)

    # We don't need to check for tensor idxs versus dims idxs here,
    # as column- versus row-stacking will never move an index for the
    # vectorized operator spaces all the way from the left to the right.
    l_mtx_dims, r_mtx_dims = map(np.product, map(flatten, contracted_dims))

    # Reshape back into a 2D matrix.
    qmtx = qtens.reshape((l_mtx_dims, r_mtx_dims))

    # Return back as a qobj.
    return Qobj(qmtx, dims=contracted_dims, superrep=qobj.superrep)
Exemplo n.º 3
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def _implicit_tensor_dimensions(dimensions):
    """
    Total flattened size and operator dimensions for operator creation routines
    that automatically perform tensor products.

    Parameters
    ----------
    dimensions : (int) or (list of int) or (list of list of int)
        First dimension of an operator which can create an implicit tensor
        product.  If the type is `int`, it is promoted first to `[dimensions]`.
        From there, it should be one of the two-elements `dims` parameter of a
        `qutip.Qobj` representing an `oper` or `super`, with possible tensor
        products.

    Returns
    -------
    size : int
        Dimension of backing matrix required to represent operator.
    dimensions : list
        Dimension list in the form required by ``Qobj`` creation.
    """
    if not isinstance(dimensions, list):
        dimensions = [dimensions]
    flat = flatten(dimensions)
    if not all(isinstance(x, numbers.Integral) and x >= 0 for x in flat):
        raise ValueError("All dimensions must be integers >= 0")
    return np.prod(flat), [dimensions, dimensions]
Exemplo n.º 4
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def test_dims_idxs_to_tensor_idxs():
    # Dims for a superoperator acting on linear operators on C^2 x C^3.
    dims = [[[2, 3], [2, 3]], [[2, 3], [2, 3]]]
    # Should swap the input and output subspaces of the left and right dims.
    assert_equal(
        dims_idxs_to_tensor_idxs(dims, list(range(len(flatten(dims))))),
        [2, 3, 0, 1, 6, 7, 4, 5])
Exemplo n.º 5
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def test_dims_idxs_to_tensor_idxs():
    # Dims for a superoperator acting on linear operators on C^2 x C^3.
    dims = [[[2, 3], [2, 3]], [[2, 3], [2, 3]]]
    # Should swap the input and output subspaces of the left and right dims.
    assert_equal(
        dims_idxs_to_tensor_idxs(dims, list(range(len(flatten(dims))))),
        [2, 3, 0, 1, 6, 7, 4, 5]
    )
Exemplo n.º 6
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def test_deep_map(base, mapping):
    """
    Test the deep mapping.  To simplify generation of edge-cases, this tests
    against an equivalent (but slower) operation of flattening and unflattening
    the list.  We can get false negatives if the `flatten` or `unflatten`
    functions are broken, but other tests should catch those.
    """
    # This function might not need to be public, and consequently might not
    # need to be tested here.
    labels = enumerate_flat(base)
    expected = unflatten([mapping(x) for x in flatten(base)], labels)
    assert deep_map(mapping, base) == expected
Exemplo n.º 7
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def tensor_swap(q_oper, *pairs):
    """Transposes one or more pairs of indices of a Qobj.
    Note that this uses dense representations and thus
    should *not* be used for very large Qobjs.

    Parameters
    ----------

    pairs : tuple
        One or more tuples ``(i, j)`` indicating that the
        ``i`` and ``j`` dimensions of the original qobj
        should be swapped.

    Returns
    -------

    sqobj : Qobj
        The original Qobj with all named index pairs swapped with each other
    """
    dims = q_oper.dims
    tensor_pairs = dims_idxs_to_tensor_idxs(dims, pairs)

    data = q_oper.data.toarray()

    # Reshape into tensor indices
    data = data.reshape(dims_to_tensor_shape(dims))

    # Now permute the dims list so we know how to get back.
    flat_dims = flatten(dims)
    perm = list(range(len(flat_dims)))
    for i, j in pairs:
        flat_dims[i], flat_dims[j] = flat_dims[j], flat_dims[i]
    for i, j in tensor_pairs:
        perm[i], perm[j] = perm[j], perm[i]
    dims = unflatten(flat_dims, enumerate_flat(dims))

    # Next, permute the actual indices of the dense tensor.
    data = data.transpose(perm)

    # Reshape back, using the left and right of dims.
    data = data.reshape(list(map(np.prod, dims)))

    return Qobj(inpt=data, dims=dims, superrep=q_oper.superrep)
Exemplo n.º 8
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def tensor_swap(q_oper, *pairs):
    """Transposes one or more pairs of indices of a Qobj.
    Note that this uses dense representations and thus
    should *not* be used for very large Qobjs.

    Parameters
    ----------

    pairs : tuple
        One or more tuples ``(i, j)`` indicating that the
        ``i`` and ``j`` dimensions of the original qobj
        should be swapped.

    Returns
    -------

    sqobj : Qobj
        The original Qobj with all named index pairs swapped with each other
    """
    dims = q_oper.dims
    tensor_pairs = dims_idxs_to_tensor_idxs(dims, pairs)

    data = q_oper.data.toarray()

    # Reshape into tensor indices
    data = data.reshape(dims_to_tensor_shape(dims))

    # Now permute the dims list so we know how to get back.
    flat_dims = flatten(dims)
    perm = list(range(len(flat_dims)))
    for i, j in pairs:
        flat_dims[i], flat_dims[j] = flat_dims[j], flat_dims[i]
    for i, j in tensor_pairs:
        perm[i], perm[j] = perm[j], perm[i]
    dims = unflatten(flat_dims, enumerate_flat(dims))

    # Next, permute the actual indices of the dense tensor.
    data = data.transpose(perm)

    # Reshape back, using the left and right of dims.
    data = data.reshape(list(map(np.prod, dims)))

    return Qobj(inpt=data, dims=dims, superrep=q_oper.superrep)
Exemplo n.º 9
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 def test_flatten(self, base, flat):
     assert flatten(base) == flat
Exemplo n.º 10
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 def test_dims_idxs_to_tensor_idxs(self, indices):
     test_indices = list(range(len(flatten(indices.base))))
     assert (dims_idxs_to_tensor_idxs(indices.base,
                                      test_indices) == indices.permutation)
Exemplo n.º 11
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def test_unflatten():
    l = [[[10, 20, 30], [40, 50, 60]], [[70, 80, 90], [100, 110, 120]]]
    labels = enumerate_flat(l)
    assert unflatten(flatten(l), labels) == l
Exemplo n.º 12
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def test_flatten():
    l = [[[0], 1], 2]
    assert_equal(flatten(l), [0, 1, 2])
Exemplo n.º 13
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def test_unflatten():
    l = [[[10, 20, 30], [40, 50, 60]], [[70, 80, 90], [100, 110, 120]]]
    labels = enumerate_flat(l)
    assert unflatten(flatten(l), labels) == l
Exemplo n.º 14
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def test_flatten():
    l = [[[0], 1], 2]
    assert_equal(flatten(l), [0, 1, 2])