Técnicas avanzadas para QAOA

from __future__ import annotations

import numpy as np
import rustworkx as rx
from dataclasses import dataclass
from itertools import combinations
from threading import Timer
from collections.abc import Callable, Iterable
from pysat.formula import CNF, IDPool
from pysat.solvers import Solver
from scipy.optimize import minimize
from rustworkx.visualization import mpl_draw as draw_graph

from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import QAOAAnsatz
from qiskit.circuit import QuantumCircuit, ParameterVector
from qiskit.transpiler import CouplingMap, PassManager
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit.transpiler.passes.routing.commuting_2q_gate_routing import (
    SwapStrategy,
    FindCommutingPauliEvolutions,
    Commuting2qGateRouter,
)

from qiskit_ibm_runtime import QiskitRuntimeService, Session
from qiskit_ibm_runtime import SamplerV2 as Sampler

# Instantiate runtime to access backend
service = QiskitRuntimeService()
backend = service.least_busy(
    min_num_qubits=100, operational=True, simulator=False
)
print(backend)

# Check if the coupling map is symmetric. We will add a conditional below
# to avoid over-counting edges for symmetric/bi-directional coupling maps.

backend.coupling_map.is_symmetric

n = 100
graph_100 = rx.PyGraph()
graph_100.add_nodes_from((np.arange(0, n, 1)))
w = 1.0
elist = []

for edge in backend.coupling_map:
    if (edge[0] < n) and (edge[1] < n):
        # Conditional to avoid over-counting edges
        if (
            edge[1],
            edge[0],
            w,
        ) not in elist:
            elist.append((edge[0], edge[1], w))

graph_100.add_edges_from(elist)
draw_graph(graph_100, with_labels=True)

# Construct cost hamiltonian


def build_max_cut_paulis(graph: rx.PyGraph) -> list[tuple[str, float]]:
    """Convert the graph to Pauli list.

    This function does the inverse of `build_max_cut_graph`
    """
    pauli_list = []
    for edge in list(graph.edge_list()):
        paulis = ["I"] * len(graph)
        paulis[edge[0]], paulis[edge[1]] = "Z", "Z"

        weight = graph.get_edge_data(edge[0], edge[1])

        pauli_list.append(("".join(paulis)[::-1], weight))

    return pauli_list


max_cut_paulis = build_max_cut_paulis(graph_100)

cost_hamiltonian = SparsePauliOp.from_list(max_cut_paulis)
print("Cost Function Hamiltonian:", cost_hamiltonian)

# Extract longest path with no repeated nodes
nodes = rx.longest_simple_path(graph_100)

# Collect even edges and odd edges
even_edges = [
    (nodes[i], nodes[i + 1])
    if nodes[i] < nodes[i + 1]
    else (nodes[i + 1], nodes[i])
    for i in range(0, len(nodes) - 1, 2)
]
odd_edges = [
    (nodes[i], nodes[i + 1])
    if nodes[i] < nodes[i + 1]
    else (nodes[i + 1], nodes[i])
    for i in range(1, len(nodes) - 1, 2)
]
edge_list = [
    (edge[0], edge[1]) if edge[0] < edge[1] else (edge[1], edge[0])
    for edge in graph_100.edge_list()
]

swap_strategy = SwapStrategy(CouplingMap(edge_list), (even_edges, odd_edges))

"""A class to solve the SWAP gate insertion initial mapping problem
using the SAT approach from https://arxiv.org/abs/2212.05666.
"""


@dataclass
class SATResult:
    """A data class to hold the result of a SAT solver."""

    satisfiable: bool  # Satisfiable is True if the SAT model could be solved
    # in a given time.
    solution: dict  # The solution to the SAT problem if it is satisfiable.
    mapping: list  # The mapping of nodes in the pattern graph to nodes in the
    # target graph.
    elapsed_time: float  # The time it took to solve the SAT model.


class SATMapper:
    r"""A class to introduce a SAT-approach to solve
    the initial mapping problem in SWAP gate insertion for commuting gates.

    When this pass is run on a DAG it will look for the first instance of
    :class:`.Commuting2qBlock` and use the program graph :math:`P` of this block
    of gates to find a layout for a given swap strategy. This layout is found
    with a binary search over the layers :math:`l` of the swap strategy. At each
    considered layer a subgraph isomorphism problem formulated as a SAT is solved
    by a SAT solver. Each instance is whether it is possible to embed the program
    graph :math:`P` into the effective connectivity graph :math:`C_l` that is
    achieved by applying :math:`l` layers of the swap strategy to the coupling map
    :math:`C_0` of the backend. Since solving SAT problems can be hard, a
    ``time_out`` fixes the maximum time allotted to the SAT solver for each
    instance. If this time is exceeded the considered problem is deemed
    unsatisfiable and the binary search proceeds to the next number of swap
    layers :math:``l``.
    """

    def __init__(self, timeout: int = 60):
        """Initialize the SATMapping.

        Args:
            timeout: The allowed time in seconds for each iteration of the SAT
                solver. This variable defaults to 60 seconds.
        """
        self.timeout = timeout

    def find_initial_mappings(
        self,
        program_graph: rx.Graph,
        swap_strategy: SwapStrategy,
        min_layers: int | None = None,
        max_layers: int | None = None,
    ) -> dict[int, SATResult]:
        r"""Find an initial mapping for a given swap strategy. Perform a
        binary search over the number of swap layers, and for each number
        of swap layers solve a subgraph isomorphism problem formulated as
        a SAT problem.

        Args:
            program_graph (rx.Graph): The program graph with commuting gates, where
                                        each edge represents a two-qubit gate.
            swap_strategy (SwapStrategy): The swap strategy to use to find the
                                        initial mapping.
            min_layers (int): The minimum number of swap layers to consider.
                                        Defaults to the maximum degree of the
                                        program graph - 2.
            max_layers (int): The maximum number of swap layers to consider.
                                        Defaults to the number of qubits in the
                                        swap strategy - 2.

        Returns:
            dict[int, SATResult]: A dictionary containing the results of the SAT
                                    solver for each number of swap layers.
        """
        num_nodes_g1 = len(program_graph.nodes())
        num_nodes_g2 = swap_strategy.distance_matrix.shape[0]
        if num_nodes_g1 > num_nodes_g2:
            return SATResult(False, [], [], 0)
        if min_layers is None:
            # use the maximum degree of the program graph - 2
            # as the lower bound.
            min_layers = max((d for _, d in program_graph.degree)) - 2
        if max_layers is None:
            max_layers = num_nodes_g2 - 1

        variable_pool = IDPool(start_from=1)
        variables = np.array(
            [
                [variable_pool.id(f"v_{i}_{j}") for j in range(num_nodes_g2)]
                for i in range(num_nodes_g1)
            ],
            dtype=int,
        )
        vid2mapping = {v: idx for idx, v in np.ndenumerate(variables)}
        binary_search_results = {}

        def interrupt(solver):
            # This function is called to interrupt the solver when the
            # timeout is reached.
            solver.interrupt()

        # Make a cnf (conjunctive normal form) for the one-to-one
        # mapping constraint
        cnf1 = []
        for i in range(num_nodes_g1):
            clause = variables[i, :].tolist()
            cnf1.append(clause)
            for k, m in combinations(clause, 2):
                cnf1.append([-1 * k, -1 * m])
        for j in range(num_nodes_g2):
            clause = variables[:, j].tolist()
            for k, m in combinations(clause, 2):
                cnf1.append([-1 * k, -1 * m])

        # Perform a binary search over the number of swap layers to find the
        # minimum number of swap layers that satisfies the subgraph isomorphism
        # problem.
        while min_layers < max_layers:
            num_layers = (min_layers + max_layers) // 2

            # Create the connectivity matrix. Note that if the swap strategy
            # cannot reach full connectivity then its distance matrix will have
            # entries with -1. These entries must be treated as False.
            d_matrix = swap_strategy.distance_matrix
            connectivity_matrix = (
                (-1 < d_matrix) & (d_matrix <= num_layers)
            ).astype(int)
            # Make a cnf for the adjacency constraint
            cnf2 = []
            for e_0, e_1 in list(program_graph.edge_list()):
                clause_matrix = np.multiply(
                    connectivity_matrix, variables[e_1, :]
                )
                clause = np.concatenate(
                    (
                        [[-variables[e_0, i]] for i in range(num_nodes_g2)],
                        clause_matrix,
                    ),
                    axis=1,
                )
                # Remove 0s from each clause
                cnf2.extend([c[c != 0].tolist() for c in clause])

            cnf = CNF(from_clauses=cnf1 + cnf2)

            with Solver(bootstrap_with=cnf, use_timer=True) as solver:
                # Solve the SAT problem with a timeout.
                # Timer is used to interrupt the solver when the
                # timeout is reached.
                timer = Timer(self.timeout, interrupt, [solver])
                timer.start()
                status = solver.solve_limited(expect_interrupt=True)
                timer.cancel()
                # Get the solution and the elapsed time.
                sol = solver.get_model()
                e_time = solver.time()

                print(
                    f"Layers: {num_layers}, Status: {status}, Time: {e_time}"
                )
                if status:
                    # If the SAT problem is satisfiable, convert the solution
                    # to a mapping.
                    mapping = [vid2mapping[idx] for idx in sol if idx > 0]
                    binary_search_results[num_layers] = SATResult(
                        status, sol, mapping, e_time
                    )
                    max_layers = num_layers
                else:
                    # If the SAT problem is unsatisfiable, return the last
                    # satisfiable solution.
                    binary_search_results[num_layers] = SATResult(
                        status, sol, [], e_time
                    )
                    min_layers = num_layers + 1

        return binary_search_results

    def remap_graph_with_sat(
        self, graph: rx.Graph, swap_strategy, max_layers
    ):
        """Applies the SAT mapping.

        Args:
            graph (nx.Graph): The graph to remap.
            swap_strategy (SwapStrategy): The swap strategy to use
                                            to find the initial mapping.

        Returns:
            tuple: A tuple containing the remapped graph, the edge map, and the
            number of layers of the swap strategy that was used to find the
            initial mapping. If no solution is found then the tuple contains
            None for each element. Note the returned edge map `{k: v}` means that
            node `k` in the original graph gets mapped to node `v` in the
            Pauli strings.
        """
        num_nodes = len(graph.nodes())
        results = self.find_initial_mappings(
            graph, swap_strategy, 0, max_layers
        )
        solutions = [k for k, v in results.items() if v.satisfiable]

        if len(solutions):
            min_k = min(solutions)
            edge_map = dict(results[min_k].mapping)
            # Create the remapped graph
            remapped_graph = rx.PyGraph()
            remapped_graph.add_nodes_from(range(num_nodes))
            mapping = dict(results[min_k].mapping)
            for i, graph_edge in enumerate(list(graph.edge_list())):
                remapped_edge = tuple(mapping[node] for node in graph_edge)
                remapped_graph.add_edge(*remapped_edge, graph.edges()[i])
            return remapped_graph, edge_map, min_k
        else:
            return None, None, None

sm = SATMapper(timeout=10)
remapped_graph, edge_map, min_swap_layers = sm.remap_graph_with_sat(
    graph=graph_100, swap_strategy=swap_strategy, max_layers=1
)
print("Map from old to new nodes: ", edge_map)
print("Min SWAP layers:", min_swap_layers)
draw_graph(remapped_graph, node_size=200, with_labels=True, width=1)

remapped_max_cut_paulis = build_max_cut_paulis(remapped_graph)
# define a qiskit SparsePauliOp from the list of paulis
remapped_cost_operator = SparsePauliOp.from_list(remapped_max_cut_paulis)
print(remapped_cost_operator)

def make_meas_map(circuit: QuantumCircuit) -> dict:
    """Return a mapping from qubit index (the key) to classical bit (the value).

    This allows us to account for the swapping order introduced by the
    SWAP strategy.
    """
    creg = circuit.cregs[0]
    qreg = circuit.qregs[0]

    meas_map = {}
    for inst in circuit.data:
        if inst.operation.name == "measure":
            meas_map[qreg.index(inst.qubits[0])] = creg.index(inst.clbits[0])

    return meas_map


def apply_swap_strategy(
    circuit: QuantumCircuit,
    swap_strategy: SwapStrategy,
    edge_coloring: dict[tuple[int, int], int] | None = None,
) -> QuantumCircuit:
    """Transpile with a SWAP strategy.

    Returns:
        A quantum circuit transpiled with the given swap strategy.
    """

    pm_pre = PassManager(
        [
            FindCommutingPauliEvolutions(),
            Commuting2qGateRouter(
                swap_strategy,
                edge_coloring,
            ),
        ]
    )
    return pm_pre.run(circuit)


def apply_qaoa_layers(
    cost_layer: QuantumCircuit,
    meas_map: dict,
    num_layers: int,
    gamma: list[float] | ParameterVector = None,
    beta: list[float] | ParameterVector = None,
    initial_state: QuantumCircuit = None,
    mixer: QuantumCircuit = None,
):
    """Applies QAOA layers to construct circuit.

    First, the initial state is applied. If `initial_state` is None, we begin in
    the initial superposition state. Next, we alternate between layers of the
    cost operator and the mixer. The cost operator is alternatively applied in
    order and in reverse instruction order. This allows us to apply the swap
    strategy on odd `p` layers and undo the swap strategy on even `p` layers.
    """

    num_qubits = cost_layer.num_qubits
    new_circuit = QuantumCircuit(num_qubits, num_qubits)

    if initial_state is not None:
        new_circuit.append(initial_state, range(num_qubits))
    else:
        # all h state by default
        new_circuit.h(range(num_qubits))

    if gamma is None or beta is None:
        gamma = ParameterVector("γ'", num_layers)
        if mixer is None or mixer.num_parameters == 0:
            beta = ParameterVector("β'", num_layers)
        else:
            beta = ParameterVector("β'", num_layers * mixer.num_parameters)

    if mixer is not None:
        mixer_layer = mixer
    else:
        mixer_layer = QuantumCircuit(num_qubits)
        mixer_layer.rx(-2 * beta[0], range(num_qubits))

    for layer in range(num_layers):
        bind_dict = {cost_layer.parameters[0]: gamma[layer]}
        cost_layer_ = cost_layer.assign_parameters(bind_dict)
        bind_dict = {
            mixer_layer.parameters[i]: beta[layer + i]
            for i in range(mixer_layer.num_parameters)
        }
        layer_mixer = mixer_layer.assign_parameters(bind_dict)

        if layer % 2 == 0:
            new_circuit.append(cost_layer_, range(num_qubits))
        else:
            new_circuit.append(cost_layer_.reverse_ops(), range(num_qubits))

        new_circuit.append(layer_mixer, range(num_qubits))

    for qidx, cidx in meas_map.items():
        new_circuit.measure(qidx, cidx)

    return new_circuit


def create_qaoa_swap_circuit(
    cost_operator: SparsePauliOp,
    swap_strategy: SwapStrategy,
    edge_coloring: dict = None,
    theta: list[float] = None,
    qaoa_layers: int = 1,
    initial_state: QuantumCircuit = None,
    mixer: QuantumCircuit = None,
):
    """Create the circuit for QAOA.

    Notes: This circuit construction for QAOA works for quadratic terms in `Z`
    and will be extended to first-order terms in `Z`.
    Higher-orders are not supported.

    Args:
        cost_operator: the cost operator.
        swap_strategy: selected swap strategy
        edge_coloring: A coloring of edges that should correspond to the
            coupling map of the hardware. It defines the order in which
            we apply the Rzz gates. This allows us to choose an ordering
            such that `Rzz` gates will immediately precede SWAP gates
            to leverage CNOT cancellation.
        theta: The QAOA angles.
        qaoa_layers: The number of layers of the cost operator and the
        mixer operator.
        initial_state: The initial state on which we apply layers of
            cost operator and mixer.
        mixer: The QAOA mixer. It will be applied as is onto the QAOA
            circuit. Therefore, its output must have the same ordering
            of qubits as its input.
    """

    num_qubits = cost_operator.num_qubits

    if theta is not None:
        gamma = theta[: len(theta) // 2]
        beta = theta[len(theta) // 2 :]
        qaoa_layers = len(theta) // 2
    else:
        gamma = beta = None

    # First, create the ansatz of one layer of QAOA without mixer
    cost_layer = QAOAAnsatz(
        cost_operator,
        reps=1,
        initial_state=QuantumCircuit(num_qubits),
        mixer_operator=QuantumCircuit(num_qubits),
    ).decompose()

    # This will allow us to recover the permutation of the measurements
    # that the swaps introduce.
    cost_layer.measure_all()

    # Now, apply the swap strategy for commuting gates
    cost_layer = apply_swap_strategy(cost_layer, swap_strategy, edge_coloring)

    # Compute the measurement map (qubit to classical bit).
    # We will apply this for odd layers where the swaps were inserted.
    if qaoa_layers % 2 == 1:
        meas_map = make_meas_map(cost_layer)
    else:
        meas_map = {idx: idx for idx in range(num_qubits)}

    cost_layer.remove_final_measurements()

    # Finally, introduce the mixer circuit and add measurements
    # following measurement map
    circuit = apply_qaoa_layers(
        cost_layer, meas_map, qaoa_layers, gamma, beta, initial_state, mixer
    )

    return circuit

# We can define the edge_coloring map so that RZZ gates are positioned
# right before SWAP gates to exploit CX cancellations
# We use greedy edge coloring from rustworkx to color the edges of
# the graph. This coloring is used to order the RZZ gates
# in the circuit.

edge_coloring_idx = rx.graph_greedy_edge_color(graph_100)
edge_coloring = {
    edge: edge_coloring_idx[idx]
    for idx, edge in enumerate(list(graph_100.edge_list()))
}
edge_coloring = {tuple(sorted(k)): v for k, v in edge_coloring.items()}

qaoa_circ = create_qaoa_swap_circuit(
    remapped_cost_operator,
    swap_strategy,
    edge_coloring=edge_coloring,
    qaoa_layers=1,
)
qaoa_circ.draw(output="mpl", fold=False)

pass_manager = generate_preset_pass_manager(
    backend=backend,
    optimization_level=3,
)

transpiled_qaoa_circ = pass_manager.run(qaoa_circ)

# Utility functions for the evaluation of the expectation value of a measured state
# In this code, for optimization, the measured state is converted into a bit string,
# and the sign of the value is determined by taking the exclusive OR of the bits
# corresponding to Pauli Z.

_PARITY = np.array(
    [-1 if bin(i).count("1") % 2 else 1 for i in range(256)],
    dtype=np.complex128,
)


def evaluate_sparse_pauli(state: int, observable: SparsePauliOp) -> complex:
    """Utility for the evaluation of the expectation value
        of a measured state.

    Args:
        state (int): The measured state.
        observable (SparsePauliOp): The observable to evaluate the
        expectation value for.

    Returns:
        complex: The expectation value of the measured state.
    """
    packed_uint8 = np.packbits(
        observable.paulis.z, axis=1, bitorder="little"
    )  # convert observable to array with 8 bit integer
    state_bytes = np.frombuffer(
        state.to_bytes(packed_uint8.shape[1], "little"),
        dtype=np.uint8,  # convert bitstring to array with 8 bit integer
    )
    reduced = np.bitwise_xor.reduce(packed_uint8 & state_bytes, axis=1)
    # take bitwise xor of the result of 'and' conditional on the
    # above two, return 0 or 1
    return np.sum(observable.coeffs * _PARITY[reduced])

def qaoa_sampler_cost_fun(
    params, ansatz, hamiltonian, sampler, aggregation=None
):
    """Standard sampler-based QAOA cost function to be plugged into
        optimizer routines.

    Args:
        params (np.ndarray): Parameters for the ansatz.
        ansatz (QuantumCircuit): Ansatz circuit.
        hamiltonian (SparsePauliOp): Hamiltonian to be minimized.
        sampler (QAOASampler): Sampler to be used.
        aggregation (Callable | float | None): Aggregation function
            to be applied to the sampled results. If None, the sum
            of the expectation values is returned.
            If float, the CVaR with the given alpha is used.
    """
    # Run the circuit
    job = sampler.run([(ansatz, params)])
    sampler_result = job.result()
    sampled_int_counts = sampler_result[
        0
    ].data.c.get_int_counts()  # bitstrings are stored as integers
    shots = sum(sampled_int_counts.values())
    int_count_distribution = {
        key: val / shots for key, val in sampled_int_counts.items()
    }

    # a dictionary containing: {state: (measurement probability, value)}
    evaluated = {
        state: (
            probability,
            np.real(evaluate_sparse_pauli(state, hamiltonian)),
        )
        for state, probability in int_count_distribution.items()
    }

    # If aggregation is None, return the sum of the expectation values.
    # If aggregation is a float, return the CVaR with the given alpha.
    # Otherwise, use the aggregation function.
    if aggregation is None:
        result = sum(
            probability * value for probability, value in evaluated.values()
        )
    elif isinstance(aggregation, float):
        cvar_aggregation = _get_cvar_aggregation(aggregation)
        result = cvar_aggregation(evaluated.values())
    else:
        result = aggregation(evaluated.values())

    global iter_counts, result_dict
    iter_counts += 1
    temp_dict = {}
    temp_dict["params"] = params.tolist()
    temp_dict["cvar_fval"] = result
    temp_dict["fval"] = sum(
        probability * value for probability, value in evaluated.values()
    )
    temp_dict["distribution"] = sampled_int_counts
    temp_dict["evaluated"] = evaluated
    result_dict[iter_counts] = temp_dict
    print(f"Iteration {iter_counts}: {result}")

    return result


def _get_cvar_aggregation(alpha: float | None) -> Callable:
    """Return the CVaR aggregation function with the given alpha.

    Args:
        alpha (float | None): Alpha value for the CVaR aggregation.
            If None, 1 is used by default.
    Raises:
        ValueError: If alpha is not in [0, 1].
    """
    if alpha is None:
        alpha = 1
    elif not 0 <= alpha <= 1:
        raise ValueError(f"alpha must be in [0, 1], but {alpha} was given.")

    def cvar_aggregation(
        objective_dict: Iterable[tuple[float, float]],
    ) -> float:
        """Return the CVaR of the given measurements.
        Args:
            objective_dict (Iterable[tuple[float, float]]): An iterable
                of tuples containing the measured bit string and the
                objective value based on the bit string.

        """
        sorted_measurements = sorted(objective_dict, key=lambda x: x[1])
        # accumulate the probabilities until alpha is reached
        accumulated_percent = 0.0
        cvar = 0.0
        for probability, value in sorted_measurements:
            cvar += value * min(probability, alpha - accumulated_percent)
            accumulated_percent += probability
            if accumulated_percent >= alpha:
                break
        return cvar / alpha

    return cvar_aggregation

num_2q_ops = transpiled_qaoa_circ.count_ops()[
    "cz"
]  # the two qubit gates on our backend are cz's.

for el in backend.properties().general:
    if el.name[:2] == "lf" and el.name[3:] == str(
        n
    ):  # pick out lf_100, lf of the best 100q chain
        lf = el.value  # layer fidelity
        print("layer fidelity", lf)
        eplg = 1 - lf ** (1 / (n - 1))  # error per layered gate (EPLG)
        fid_cz = 1 - eplg
        gamma_cz = 1 / fid_cz**2
        gamma_circ = gamma_cz**num_2q_ops

cvar_aggregation = 1 / np.sqrt(gamma_circ)
print("")
print("The corresponding CVaR aggregation value is: ", cvar_aggregation)
print(
    "To mitigate the twirled noise, increase shots by a factor of",
    np.sqrt(gamma_circ),
)

iter_counts = 0
result_dict = {}
init_params = [np.pi, np.pi / 2]

with Session(backend=backend) as session:
    sampler = Sampler(mode=session)
    sampler.options.default_shots = int(1000 / cvar_aggregation)
    sampler.options.dynamical_decoupling.enable = True
    sampler.options.dynamical_decoupling.sequence_type = "XY4"
    sampler.options.twirling.enable_gates = True
    sampler.options.twirling.enable_measure = True
    sampler.options.environment.job_tags = [
        "TUT_AQAOA"
    ]  # add tag for your job execution

    result = minimize(
        qaoa_sampler_cost_fun,
        init_params,
        args=(
            transpiled_qaoa_circ,
            remapped_cost_operator,
            sampler,
            cvar_aggregation,
        ),
        method="COBYLA",
        tol=1e-2,
    )
print(result)

from matplotlib import pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(
    [result_dict[i]["cvar_fval"] for i in range(1, iter_counts + 1)],
    label="CVaR",
)
plt.plot(
    [result_dict[i]["fval"] for i in range(1, iter_counts + 1)],
    label="Standard",
)
plt.legend()
plt.xlabel("Iteration")
plt.ylabel("Cost")
plt.show()

# Sort the result_dict[iter_counts]['evaluated'] by the CVaR value
sorted_result_dict = [
    (k, v)
    for k, v in sorted(
        result_dict[iter_counts]["evaluated"].items(),
        key=lambda item: item[1][1],
    )
]
print(
    f"bitstring (int): {sorted_result_dict[0][0]}, "
    f"probability: {sorted_result_dict[0][1][0]}, "
    f"objective value: {sorted_result_dict[0][1][1]}"
)

from typing import Sequence


def to_bitstring(integer, num_bits):
    result = np.binary_repr(integer, width=num_bits)
    return [int(digit) for digit in result]


def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:
    assert len(x) == len(
        list(graph.nodes())
    ), "The length of x must coincide with the number of nodes in the graph."
    return sum(
        x[u] * (1 - x[v])
        + x[v]
        * (
            1 - x[u]
        )  # x[u] = x[v] if same cut, x[u] \neq x[v] if different cuts
        for u, v in list(graph.edge_list())
    )


bitstring = to_bitstring(
    sorted_result_dict[0][0], len(list(remapped_graph.nodes()))
)
bitstring = bitstring[::-1]
print(f"Result bitstring (binary) : {bitstring}")

cut_value = evaluate_sample(bitstring, remapped_graph)
print(f"The value of the cut is: {cut_value}")

def plot_result(G, x):
    colors = ["tab:grey" if i == 0 else "tab:purple" for i in x]
    pos, _default_axes = rx.spring_layout(G), plt.axes(frameon=True)
    rx.visualization.mpl_draw(
        G,
        node_color=colors,
        node_size=150,
        alpha=0.8,
        pos=pos,
        with_labels=True,
        width=1,
    )


plot_result(graph_100, to_bitstring(sorted_result_dict[0][0], 100)[::-1])