# Buggy code
def solve(self, problem: QuadraticProgram) -> OptimizationResult:
    """Tries to solves the given problem using the grover optimizer.

    Runs the optimizer to try to solve the optimization problem. If the problem cannot be,
    converted to a QUBO, this optimizer raises an exception due to incompatibility.

    Args:
        problem: The problem to be solved.

    Returns:
        The result of the optimizer applied to the problem.

    Raises:
        AttributeError: If the quantum instance has not been set.
        QiskitOptimizationError: If the problem is incompatible with the optimizer.
    """
    if self.quantum_instance is None:
        raise AttributeError("The quantum instance or backend has not been set.")

    self._verify_compatibility(problem)

    # convert problem to QUBO
    problem_ = self._convert(problem, self._converters)
    problem_init = deepcopy(problem_)

    # convert to minimization problem
    sense = problem_.objective.sense
    if sense == problem_.objective.Sense.MAXIMIZE:
        problem_.objective.sense = problem_.objective.Sense.MINIMIZE
        problem_.objective.constant = -problem_.objective.constant
        for i, val in problem_.objective.linear.to_dict().items():
            problem_.objective.linear[i] = -val
        for (i, j), val in problem_.objective.quadratic.to_dict().items():
            problem_.objective.quadratic[i, j] = -val
    self._num_key_qubits = len(problem_.objective.linear.to_array())  # type: ignore

    # Variables for tracking the optimum.
    optimum_found = False
    optimum_key = math.inf
    optimum_value = math.inf
    threshold = 0
    n_key = len(problem_.variables)
    n_value = self._num_value_qubits

    # Variables for tracking the solutions encountered.
    num_solutions = 2**n_key
    keys_measured = []

    # Variables for result object.
    operation_count = {}
    iteration = 0

    # Variables for stopping if we've hit the rotation max.
    rotations = 0
    max_rotations = int(np.ceil(100 * np.pi / 4))

    # Initialize oracle helper object.
    qr_key_value = QuantumRegister(self._num_key_qubits + self._num_value_qubits)
    orig_constant = problem_.objective.constant
    measurement = not self.quantum_instance.is_statevector
    oracle, is_good_state = self._get_oracle(qr_key_value)

    while not optimum_found:
        m = 1
        improvement_found = False

        # Get oracle O and the state preparation operator A for the current threshold.
        problem_.objective.constant = orig_constant - threshold
        a_operator = self._get_a_operator(qr_key_value, problem_)

        # Iterate until we measure a negative.
        loops_with_no_improvement = 0
        while not improvement_found:
            # Determine the number of rotations.
            loops_with_no_improvement += 1
            rotation_count = int(np.ceil(aqua_globals.random.uniform(0, m - 1)))
            rotations += rotation_count

            # Apply Grover's Algorithm to find values below the threshold.
            if rotation_count > 0:
                # TODO: Utilize Grover's incremental feature - requires changes to Grover.
                grover = Grover(
                    oracle, state_preparation=a_operator, good_state=is_good_state
                )
                circuit = grover.construct_circuit(
                    rotation_count, measurement=measurement
                )
            else:
                circuit = a_operator

            # Get the next outcome.
            outcome = self._measure(circuit)
            k = int(outcome[0:n_key], 2)
            v = outcome[n_key : n_key + n_value]
            int_v = self._bin_to_int(v, n_value) + threshold
            v = self._twos_complement(int_v, n_value)
            logger.info("Outcome: %s", outcome)
            logger.info("Value: %s = %s", v, int_v)

            # If the value is an improvement, we update the iteration parameters (e.g. oracle).
            if int_v < optimum_value:
                optimum_key = k
                optimum_value = int_v
                logger.info("Current Optimum Key: %s", optimum_key)
                logger.info("Current Optimum Value: %s", optimum_value)
                if v.startswith("1"):
                    improvement_found = True
                    threshold = optimum_value
            else:
                # Using Durr and Hoyer method, increase m.
                m = int(np.ceil(min(m * 8 / 7, 2 ** (n_key / 2))))
                logger.info("No Improvement. M: %s", m)

                # Check if we've already seen this value.
                if k not in keys_measured:
                    keys_measured.append(k)

                # Assume the optimal if any of the stop parameters are true.
                if (
                    loops_with_no_improvement >= self._n_iterations
                    or len(keys_measured) == num_solutions
                    or rotations >= max_rotations
                ):
                    improvement_found = True
                    optimum_found = True

            # Track the operation count.
            operations = circuit.count_ops()
            operation_count[iteration] = operations
            iteration += 1
            logger.info("Operation Count: %s\n", operations)

    # If the constant is 0 and we didn't find a negative, the answer is likely 0.
    if optimum_value >= 0 and orig_constant == 0:
        optimum_key = 0

    opt_x = np.array(
        [1 if s == "1" else 0 for s in ("{0:%sb}" % n_key).format(optimum_key)]
    )

    # Compute function value
    fval = problem_init.objective.evaluate(opt_x)
    result = OptimizationResult(
        x=opt_x,
        fval=fval,
        variables=problem_.variables,
        status=OptimizationResultStatus.SUCCESS,
    )

    # cast binaries back to integers
    result = self._interpret(result, self._converters)

    return GroverOptimizationResult(
        x=result.x,
        fval=result.fval,
        variables=result.variables,
        operation_counts=operation_count,
        n_input_qubits=n_key,
        n_output_qubits=n_value,
        intermediate_fval=fval,
        threshold=threshold,
        status=self._get_feasibility_status(problem, result.x),
    )


# Traceback (IndexError)
No classical registers in circuit "circuit9", counts will be empty.
Traceback (most recent call last):
File "scratch.py", line 101, in <module>
results = grover_optimizer.solve(qp)
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py", line 218, in solve
outcome = self._measure(circuit)
File "/home/will/miniconda3/envs/dev/lib/python3.8/site-packages/qiskit/optimization/algorithms/grover_optimizer.py", line 283, in _measure
freq[-1] = (freq[-1][0], 1.0 - sum(x[1] for x in freq[0:len(freq) - 1]))
IndexError: list index out of range

# Fixed code
def solve(self, problem: QuadraticProgram) -> OptimizationResult:
    """Tries to solves the given problem using the grover optimizer.

    Runs the optimizer to try to solve the optimization problem. If the problem cannot be,
    converted to a QUBO, this optimizer raises an exception due to incompatibility.

    Args:
        problem: The problem to be solved.

    Returns:
        The result of the optimizer applied to the problem.

    Raises:
        AttributeError: If the quantum instance has not been set.
        QiskitOptimizationError: If the problem is incompatible with the optimizer.
    """
    if self.quantum_instance is None:
        raise AttributeError("The quantum instance or backend has not been set.")

    self._verify_compatibility(problem)

    # convert problem to QUBO
    problem_ = self._convert(problem, self._converters)
    problem_init = deepcopy(problem_)

    # convert to minimization problem
    sense = problem_.objective.sense
    if sense == problem_.objective.Sense.MAXIMIZE:
        problem_.objective.sense = problem_.objective.Sense.MINIMIZE
        problem_.objective.constant = -problem_.objective.constant
        for i, val in problem_.objective.linear.to_dict().items():
            problem_.objective.linear[i] = -val
        for (i, j), val in problem_.objective.quadratic.to_dict().items():
            problem_.objective.quadratic[i, j] = -val
    self._num_key_qubits = len(problem_.objective.linear.to_array())  # type: ignore

    # Variables for tracking the optimum.
    optimum_found = False
    optimum_key = math.inf
    optimum_value = math.inf
    threshold = 0
    n_key = len(problem_.variables)
    n_value = self._num_value_qubits

    # Variables for tracking the solutions encountered.
    num_solutions = 2**n_key
    keys_measured = []

    # Variables for result object.
    operation_count = {}
    iteration = 0

    # Variables for stopping if we've hit the rotation max.
    rotations = 0
    max_rotations = int(np.ceil(100 * np.pi / 4))

    # Initialize oracle helper object.
    qr_key_value = QuantumRegister(self._num_key_qubits + self._num_value_qubits)
    orig_constant = problem_.objective.constant
    measurement = not self.quantum_instance.is_statevector
    oracle, is_good_state = self._get_oracle(qr_key_value)

    while not optimum_found:
        m = 1
        improvement_found = False

        # Get oracle O and the state preparation operator A for the current threshold.
        problem_.objective.constant = orig_constant - threshold
        a_operator = self._get_a_operator(qr_key_value, problem_)

        # Iterate until we measure a negative.
        loops_with_no_improvement = 0
        while not improvement_found:
            # Determine the number of rotations.
            loops_with_no_improvement += 1
            rotation_count = int(np.ceil(aqua_globals.random.uniform(0, m - 1)))
            rotations += rotation_count
            # Apply Grover's Algorithm to find values below the threshold.
            # TODO: Utilize Grover's incremental feature - requires changes to Grover.
            grover = Grover(
                oracle, state_preparation=a_operator, good_state=is_good_state
            )
            circuit = grover.construct_circuit(rotation_count, measurement=measurement)

            # Get the next outcome.
            outcome = self._measure(circuit)
            k = int(outcome[0:n_key], 2)
            v = outcome[n_key : n_key + n_value]
            int_v = self._bin_to_int(v, n_value) + threshold
            v = self._twos_complement(int_v, n_value)
            logger.info("Outcome: %s", outcome)
            logger.info("Value: %s = %s", v, int_v)

            # If the value is an improvement, we update the iteration parameters (e.g. oracle).
            if int_v < optimum_value:
                optimum_key = k
                optimum_value = int_v
                logger.info("Current Optimum Key: %s", optimum_key)
                logger.info("Current Optimum Value: %s", optimum_value)
                if v.startswith("1"):
                    improvement_found = True
                    threshold = optimum_value
            else:
                # Using Durr and Hoyer method, increase m.
                m = int(np.ceil(min(m * 8 / 7, 2 ** (n_key / 2))))
                logger.info("No Improvement. M: %s", m)

                # Check if we've already seen this value.
                if k not in keys_measured:
                    keys_measured.append(k)

                # Assume the optimal if any of the stop parameters are true.
                if (
                    loops_with_no_improvement >= self._n_iterations
                    or len(keys_measured) == num_solutions
                    or rotations >= max_rotations
                ):
                    improvement_found = True
                    optimum_found = True

            # Track the operation count.
            operations = circuit.count_ops()
            operation_count[iteration] = operations
            iteration += 1
            logger.info("Operation Count: %s\n", operations)

    # If the constant is 0 and we didn't find a negative, the answer is likely 0.
    if optimum_value >= 0 and orig_constant == 0:
        optimum_key = 0

    opt_x = np.array(
        [1 if s == "1" else 0 for s in ("{0:%sb}" % n_key).format(optimum_key)]
    )

    # Compute function value
    fval = problem_init.objective.evaluate(opt_x)
    result = OptimizationResult(
        x=opt_x,
        fval=fval,
        variables=problem_.variables,
        status=OptimizationResultStatus.SUCCESS,
    )

    # cast binaries back to integers
    result = self._interpret(result, self._converters)

    return GroverOptimizationResult(
        x=result.x,
        fval=result.fval,
        variables=result.variables,
        operation_counts=operation_count,
        n_input_qubits=n_key,
        n_output_qubits=n_value,
        intermediate_fval=fval,
        threshold=threshold,
        status=self._get_feasibility_status(problem, result.x),
    )