Exemple #1
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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)
Exemple #2
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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)
Exemple #3
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def test_dims_to_tensor_shape():
    # Dims for a superoperator:
    #     L(L(C⁰ × C¹, C² × C³), L(C³ × C⁴, C⁵ × C⁶)),
    # where L(X, Y) is a linear operator from X to Y (dims [Y, X]).
    in_dims = [[2, 3], [0, 1]]
    out_dims = [[3, 4], [5, 6]]
    dims = [out_dims, in_dims]

    # To make the expected shape, we want the left and right spaces to each
    # be flipped, then the whole thing flattened.
    shape = (5, 6, 3, 4, 0, 1, 2, 3)

    assert_equal(dims_to_tensor_shape(dims), shape)
def test_dims_to_tensor_shape():
    # Dims for a superoperator:
    #     L(L(C⁰ × C¹, C² × C³), L(C³ × C⁴, C⁵ × C⁶)),
    # where L(X, Y) is a linear operator from X to Y (dims [Y, X]).
    in_dims  = [[2, 3], [0, 1]]
    out_dims = [[3, 4], [5, 6]]
    dims = [out_dims, in_dims]

    # To make the expected shape, we want the left and right spaces to each
    # be flipped, then the whole thing flattened.
    shape = (5, 6, 3, 4, 0, 1, 2, 3)

    assert_equal(
        dims_to_tensor_shape(dims),
        shape
    )
Exemple #5
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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)
Exemple #6
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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)
Exemple #7
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 def test_dims_to_tensor_shape(self, indices):
     assert dims_to_tensor_shape(indices.base) == indices.shape