abp/abp/graphstate.py

"""
This module implements Anders and Briegel's method for fast simulation of Clifford circuits.
"""

import itertools as it
import json, random
import qi, clifford, util
from stabilizer import Stabilizer

class GraphState(object):

    """
    This is the main class used to model stabilizer states.
    Internally it uses the same dictionary-of-dictionaries data structure as ``networkx``.
    """

    def __init__(self, nodes=[], deterministic=False):
        """ Construct a ``GraphState``

        :param nodes: An iterable of nodes used to construct the graph.
        :param deterministic: If ``True``, the behaviour of the graph is deterministic up to but not including the choice of measurement outcome. This is slightly less efficient, but useful for testing. If ``False``, the specific graph representation will sometimes be random -- of course, all possible representations still map to the same state vector.
        """

        self.adj, self.node = {}, {}
        self.add_nodes(nodes)
        self.deterministic = deterministic

    def add_node(self, node, **kwargs):
        """ Add a node.

        :param node: The name of the node, e.g. ``9``, ``start``
        :type node: Any hashable type
        :param kwargs: Any extra node attributes

        Example of using node attributes ::

            >>> g.add_node(0, label="fred", position=(1,2,3))
            >>> g.node[0]["label"]
            fred

        """
        assert not node in self.node, "Node {} already exists".format(v)
        self.adj[node] = {}
        self.node[node] = {"vop": clifford.by_name["hadamard"]}
        self.node[node].update(kwargs)

    def add_nodes(self, nodes):
        """ Add many nodes in one shot. """
        for n in nodes:
            self.add_node(n)

    def act_circuit(self, circuit):
        """ Run many gates in one call.

        :param circuit: An iterable containing tuples of the form ``(node, operation)``.  If ``operation`` is a name for a local operation (e.g. ``6``, ``hadamard``) then that operation is performed on ``node``.  If ``operation`` is ``cz`` then a CZ is performed on the two nodes in ``node``.

        Example (makes a Bell pair)::

            >>> g.act_circuit([(0, "hadamard"), (1, "hadamard"), ((0, 1), "cz")])

        """
        for node, operation in circuit:
            if operation == "cz":
                self.act_cz(*node)
            else:
                self.act_local_rotation(node, operation)

    def _add_edge(self, v1, v2, data={}):
        """ Add an edge between two vertices """
        self.adj[v1][v2] = data
        self.adj[v2][v1] = data

    def _del_edge(self, v1, v2):
        """ Delete an edge between two vertices """
        del self.adj[v1][v2]
        del self.adj[v2][v1]

    def has_edge(self, v1, v2):
        """ Test existence of an edge between two vertices  """
        return v2 in self.adj[v1]

    def _toggle_edge(self, v1, v2):
        """ Toggle an edge between two vertices  """
        if self.has_edge(v1, v2):
            self._del_edge(v1, v2)
        else:
            self._add_edge(v1, v2)

    def edgelist(self):
        """ Describe a graph as an edgelist # TODO: inefficient """
        edges = set(tuple(sorted((i, n)))
                    for i, v in self.adj.items()
                    for n in v)
        return tuple(edges)

    def remove_vop(self, node, avoid):
        """ Attempts to remove the vertex operator on a particular qubit.

        :param node: The node whose vertex operator should be reduced to the identity.
        :param avoid: We will try to leave this node alone during the process (if possible).

        """
        others = set(self.adj[node]) - {avoid}
        if self.deterministic:
            swap_qubit = min(others) if others else avoid
        else:
            swap_qubit = others.pop() if others else avoid

        for v in reversed(clifford.decompositions[self.node[node]["vop"]]):
            if v == "x":
                self.local_complementation(node, "U ->")
            else:
                self.local_complementation(swap_qubit, "V ->")

    def local_complementation(self, v, prefix=""):
        """ As defined in LISTING 1 of Anders & Briegel """
        for i, j in it.combinations(self.adj[v], 2):
            self._toggle_edge(i, j)

        self.node[v]["vop"] = clifford.times_table[
            self.node[v]["vop"], clifford.by_name["msqx_h"]]
        for i in self.adj[v]:
            self.node[i]["vop"] = clifford.times_table[
                self.node[i]["vop"], clifford.by_name["sqz_h"]]

    def act_local_rotation(self, node, operation):
        """ Act a local rotation on a qubit

        :param node: The index of the node to act on
        :param operation: The Clifford-group operation to perform. You can use any of the names in the :ref:`Clifford group alias table <clifford>`.
        """
        rotation = clifford.by_name[str(operation)]
        self.node[node]["vop"] = clifford.times_table[
            rotation, self.node[node]["vop"]]

    def _update_vop(self, v, op):
        """ Update a VOP - only used internally"""
        rotation = clifford.by_name[str(op)]
        self.node[v]["vop"] = clifford.times_table[
            self.node[v]["vop"], rotation]

    def act_hadamard(self, qubit):
        """ Shorthand for ``self.act_local_rotation(qubit, "hadamard")`` """
        self.act_local_rotation(qubit, 10)

    def _lonely(self, a, b):
        """ Is this qubit _lonely ? """
        return len(self.adj[a]) > (b in self.adj[a])

    def act_cz(self, a, b):
        """ Act a controlled-phase gate on two qubits

        :param a: The first qubit
        :param b: The second qubit
        """
        if self._lonely(a, b):
            self.remove_vop(a, b)

        if self._lonely(b, a):
            self.remove_vop(b, a)

        if self._lonely(a, b) and not clifford.is_diagonal(self.node[a]["vop"]):
            self.remove_vop(a, b)

        edge = self.has_edge(a, b)
        va = self.node[a]["vop"]
        vb = self.node[b]["vop"]
        new_edge, self.node[a]["vop"], self.node[b]["vop"] = \
            clifford.cz_table[int(edge), va, vb]
        if new_edge != edge:
            self._toggle_edge(a, b)

    def measure(self, node, basis, force=None, detail=False):
        """ Measure in an arbitrary basis

        :param node: The name of the qubit to measure.
        :param basis: The basis in which to measure.
        :type basis: :math:`\in` ``{"px", "py", "pz"}``
        :param force: Forces the measurement outcome.
        :type force: boolean
        :param detail: Get detailed information. 
        :type detail: boolean
        
        Measurements in quantum mechanics are probabilistic. If you want to force a particular outcome :math:`\in\{0, 1\}`, use ``force``.

        You can get more information by setting ``detail=True``, in which case ``measure()`` returns a dictionary with the following keys:

        - ``outcome``: the measurement outcome. 
        - ``determinate``: indicates whether the outcome was determinate or random. For example, measuring :math:`|0\\rangle`  in :math:`\sigma_x` always gives a deterministic outcome. ``determinate`` is overridden by ``force`` -- forced outcomes are always determinate.
        - ``conjugated_basis``: The index of the measurement operator, rotated by the vertex operator of the measured node, i.e. :math:`U_\\text{vop} \sigma_m U_\\text{vop}^\dagger`.
        - ``phase``: The phase of the cojugated basis, :math:`\pm 1`.
        - ``node``: The name of the measured node.
        - ``force``: The value of ``force``.

        """
        basis = clifford.by_name[basis]
        ha = clifford.conjugation_table[self.node[node]["vop"]]
        basis, phase = clifford.conjugate(basis, ha)

        # Flip a coin
        result = force if force != None else random.choice([0, 1])
        # Flip the result if we have negative phase
        if phase == -1:
            result = not result

        if basis == clifford.by_name["px"]:
            result, determinate = self._measure_graph_x(node, result)
        elif basis == clifford.by_name["py"]:
            result, determinate = self._measure_graph_y(node, result)
        elif basis == clifford.by_name["pz"]:
            result, determinate = self._measure_graph_z(node, result)
        else:
            raise ValueError("You can only measure in {X,Y,Z}")

        # Flip the result if we have negative phase
        if phase == -1:
            result = not result

        if detail:
            return {"outcome": int(result), 
                    "determinate": (determinate or force!=None),
                    "conjugated_basis": basis,
                    "phase": phase,
                    "node": node,
                    "force": force}
        else:
            return int(result)

    def measure_x(self, node, force=None, detail=False):
        """ Measure in the X basis

        :param node: The name of the qubit to measure.
        :param force: Measurements in quantum mechanics are probabilistic. If you want to force a particular outcome, use the ``force``.
        :type force: boolean
        :param detail: Provide detailed information
        :type detail: boolean

        """
        return self.measure(node, "px", force, detail)

    def measure_y(self, node, force=None, detail=False):
        """ Measure in the Y basis

        :param node: The name of the qubit to measure.
        :param force: Measurements in quantum mechanics are probabilistic. If you want to force a particular outcome, use the ``force``.
        :type force: boolean
        :param detail: Provide detailed information
        :type detail: boolean

        """
        return self.measure(node, "py", force, detail)

    def measure_z(self, node, force=None, detail=False):
        """ Measure in the Z basis

        :param node: The name of the qubit to measure.
        :param force: Measurements in quantum mechanics are probabilistic. If you want to force a particular outcome, use the ``force``.
        :type force: boolean
        :param detail: Provide detailed information
        :type detail: boolean

        """
        return self.measure(node, "pz", force, detail)

    def _toggle_edges(self, a, b):
        """ Toggle edges between vertex sets a and b """
        # TODO: i'm pretty sure this is just a single-line it.combinations or
        # equiv
        done = set()
        for i, j in it.product(a, b):
            if i != j and not (i, j) in done:
                done.add((i, j))
                done.add((j, i))
                self._toggle_edge(i, j)

    def _measure_graph_x(self, node, result):
        """ Measure the bare graph in the X-basis """
        if len(self.adj[node]) == 0:
            return 0, True

        # Pick a vertex
        if self.deterministic:
            friend = sorted(self.adj[node].keys())[0]
        else:
            friend = next(self.adj[node].iterkeys())

        # Update the VOPs. TODO: pretty ugly
        if result:
            # Do a z on all ngb(vb) \ ngb(v) \ {v}, and some other stuff
            self._update_vop(friend, "msqy")
            self._update_vop(node, "pz")

            for n in set(self.adj[friend]) - set(self.adj[node]) - {node}:
                self._update_vop(n, "pz")
        else:
            # Do a z on all ngb(v) \ ngb(vb) \ {vb}, and sqy on the friend
            self._update_vop(friend, "sqy")
            for n in set(self.adj[node]) - set(self.adj[friend]) - {friend}:
                self._update_vop(n, "pz")

        # Toggle the edges. TODO: Yuk. Just awful!
        a = set(self.adj[node].keys())
        b = set(self.adj[friend].keys())
        self._toggle_edges(a, b)
        intersection = a & b
        for i, j in it.combinations(intersection, 2):
            self._toggle_edge(i, j)

        for n in a - {friend}:
            self._toggle_edge(friend, n)

        return result, False

    def _measure_graph_y(self, node, result):
        """ Measure the bare graph in the Y-basis """
        # Do some rotations
        for neighbour in self.adj[node]:
            self._update_vop(neighbour, "sqz" if result else "msqz")

        # A sort of local complementation
        vngbh = set(self.adj[node]) | {node}
        for i, j in it.combinations(vngbh, 2):
            self._toggle_edge(i, j)

        # TODO: naming: # lcoS.herm_adjoint() if result else lcoS
        self._update_vop(node, 5 if result else 6)
        return result, False

    def _measure_graph_z(self, node, result):
        """ Measure the bare graph in the Z-basis """
        # Disconnect
        for neighbour in tuple(self.adj[node]):
            self._del_edge(node, neighbour)
            if result:
                self._update_vop(neighbour, "pz")

        # Rotate
        if result:
            self._update_vop(node, "px")
            self._update_vop(node, "hadamard")
        else:
            self._update_vop(node, "hadamard")

        return result, False

    def order(self):
        """ Get the number of qubits """
        return len(self.node)

    def __str__(self):
        """ Represent as a string for quick debugging """
        s = ""
        for key in sorted(self.node.keys()):
            s += "{}:  {}\t".format(
                key, clifford.get_name(self.node[key]["vop"]).replace("YC", "-"))
            if self.adj[key]:
                s += str(tuple(self.adj[key].keys())).replace(" ", "")
            else:
                s += "-"
            s += "\n"

        return s

    def to_json(self, stringify=False):
        """ Convert the graph to JSON-like form.

        :param stringify: JSON keys must be strings, But sometimes it is useful to have a JSON-like object whose keys are tuples.

        If you want to dump a graph do disk, do something like this::

            >>> import json
            >>> with open("graph.json") as f:
                    json.dump(graph.to_json(True), f)

        .. todo::
            Implement ``from_json()``!

        """
        if stringify:
            node = {str(key): value for key, value in self.node.items()}
            adj = {str(key): {str(key): value for key, value in ngbh.items()}
                   for key, ngbh in self.adj.items()}
            return {"node": node, "adj": adj}
        else:
            return {"node": self.node, "adj": self.adj}

    def to_state_vector(self):
        """ Get the full state vector corresponding to this stabilizer state. Useful for debugging, interface with other simulators.
            This method becomes very slow for more than about ten qubits!

        The output state is represented as a ``abp.qi.CircuitModel``::

            >>> print g.to_state_vector()
            |00000>: 0.18+0.00j
            |00001>: 0.18+0.00j ...

        """
        if len(self.node) > 15:
            raise ValueError("Cannot build state vector: too many qubits")
        state = qi.CircuitModel(len(self.node))
        mapping = {node: i for i, node in enumerate(sorted(self.node))}
        for n in self.node:
            state.act_hadamard(mapping[n])
        for i, j in self.edgelist():
            state.act_cz(mapping[i], mapping[j])
        for i, n in self.node.items():
            state.act_local_rotation(mapping[i], clifford.unitaries[n["vop"]])
        return state

    def to_stabilizer(self):
        """ 
        Get the stabilizer representation of the state::

            >>> print g.to_stabilizer()
             - X I I I I
               I Z I I I
               I I Z I I
               I I I Z I
               I I I I Z

        """
        return Stabilizer(self)

    def __eq__(self, other):
        """ Check equality between GraphStates """
        return self.adj == other.adj and self.node == other.node

    def copy(self):
        """ Make a copy of this graphstate """
        g = GraphState()
        g.node = self.node.copy()
        g.adj = self.adj.copy()
        g.deterministic = self.deterministic
        return g