Source code for pennylane.ftqc.lattice
# Copyright 2025 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file defines classes and functions for creating lattice objects that store topological
connectivity information.
"""
from collections.abc import Sequence
from enum import Enum, auto
from functools import lru_cache
import networkx as nx
[docs]
class Lattice:
"""Represents a qubit lattice structure.
This Lattice class, inspired by the design of :class:`~pennylane.spin.Lattice`, leverages `NetworkX` to represent the relationships within the lattice structure.
Args:
lattice_shape: Name of the lattice shape.
graph (nx.Graph): A NetworkX undirected graph object. If provided, `nodes` and `edges` are ignored.
nodes (List): Nodes to construct a graph object. Ignored if `graph` is provided.
edges (List): Edges to construct the graph. Ignored if `graph` is provided.
Raises:
ValueError: If neither `graph` nor both `nodes` and `edges` are provided.
"""
# TODOs: To support braiding operations, Lattice should support nodes/edges addition/deletion.
def __init__(
self, lattice_shape: str, graph: nx.Graph = None, nodes: list = None, edges: list = None
):
self._lattice_shape = lattice_shape
if graph is None:
if nodes is None and edges is None:
raise ValueError(
"Neither a networkx Graph object nor nodes together with edges are provided."
)
self._graph = nx.Graph()
self._graph.add_nodes_from(nodes)
self._graph.add_edges_from(edges)
else:
self._graph = graph
@property
def shape(self) -> str:
r"""Returns the lattice shape name."""
return self._lattice_shape
[docs]
def get_neighbors(self, node):
r"""Returns the neighbors of a given node in the lattice.
Args:
node: a target node label.
"""
return self._graph.neighbors(node)
@property
def nodes(self):
r"""Returns all nodes in the lattice."""
return self._graph.nodes
@property
def edges(self):
r"""Returns all edges in the lattice."""
return self._graph.edges
@property
def graph(self) -> nx.Graph:
r"""Returns the underlying NetworkX graph object representing the lattice."""
return self._graph
class LatticeShape(Enum):
"""Enum to define valid set of lattice shape supported."""
chain = auto()
square = auto()
rectangle = auto()
triangle = auto()
honeycomb = auto()
cubic = auto()
# map between lattice name and dimensions
_LATTICE_DIM_MAP = {
"chain": 1,
"square": 2,
"rectangle": 2,
"cubic": 3,
"triangle": 2,
"honeycomb": 2,
}
# map between lattice name and networkx method name
_LATTICE_GENERATOR_MAP = {
"chain": "grid_graph",
"square": "grid_graph",
"rectangle": "grid_graph",
"cubic": "grid_graph",
"triangle": "triangular_lattice_graph",
"honeycomb": "hexagonal_lattice_graph",
}
@lru_cache
def _supported_shapes():
r"""Return the supported shape in str"""
return [shape.name for shape in LatticeShape]
[docs]
def generate_lattice(dims: Sequence[int], lattice: str) -> Lattice:
r"""Generates a :class:`~pennylane.ftqc.Lattice` object with a given geometric parameters and its shape name.
Args:
dims(List[int]): Geometric parameters for lattice generation. For lattices generated by `nx.grid_graph` ( ``'chain'``, ``'rectangle'``, ``'square'``, ``'cubic'``),
`dims` contains the number of nodes in the each direction of grid. Per ``'honeycomb'`` or ``'triangle'``, the generated lattices will have dims[0] rows and dims[1]
columns of hexagons or triangles.
lattice (str): Shape of the lattice. Input values can be ``'chain'``, ``'square'``, ``'rectangle'``, ``'honeycomb'``, ``'triangle'``, ``'cubic'``.
Returns:
a :class:`~pennylane.ftqc.Lattice` object.
Raises:
ValueError: If the lattice shape is not supported or the dimensions are invalid.
"""
lattice_shape = lattice.strip().lower()
supported_shapes = _supported_shapes()
if lattice_shape not in supported_shapes:
raise ValueError(
f"Lattice shape, '{lattice}' is not supported."
f"Please set lattice to: {supported_shapes}."
)
if _LATTICE_DIM_MAP[lattice_shape] != len(dims):
raise ValueError(
f"For a {lattice_shape} lattice, the length of dims should be {_LATTICE_DIM_MAP[lattice_shape]} instead of {len(dims)}"
)
lattice_generate_method = getattr(nx, _LATTICE_GENERATOR_MAP[lattice_shape])
if _LATTICE_GENERATOR_MAP[lattice_shape] == "grid_graph":
lattice_obj = Lattice(lattice_shape, lattice_generate_method(dims))
return lattice_obj
if _LATTICE_GENERATOR_MAP[lattice_shape] in [
"triangular_lattice_graph",
"hexagonal_lattice_graph",
]:
lattice_obj = Lattice(lattice_shape, lattice_generate_method(dims[0], dims[1]))
return lattice_obj
raise NotImplementedError # pragma: no cover
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