Source code for pennylane.ftqc.lattice

# Copyright 2025 Xanadu Quantum Technologies Inc.
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#     http://www.apache.org/licenses/LICENSE-2.0

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"""
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