def sample_state_vector(state: np.ndarray,
                        indices: List[int],
                        repetitions: int=1) -> np.ndarray:
    """Samples repeatedly from measurements in the computational basis.

    Note that this does not modify the passed in state.

    Args:
        state: The multi-qubit wavefunction to be sampled. This is an array of
            2 to the power of the number of qubit complex numbers, and so
            state must be of size ``2**integer``.  The state can be a vector of
            size ``2**integer`` or a tensor of shape ``(2, 2, ..., 2)``.
        indices: Which qubits are measured. The state is assumed to be supplied
            in big endian order. That is the xth index of v, when expressed as
            a bitstring, has its largest values in the 0th index.
        repetitions: The number of times to sample the state.

    Returns:
        Measurement results with True corresponding to the ``|1⟩`` state.
        The outer list is for repetitions, and the inner corresponds to
        measurements ordered by the supplied qubits. These lists
        are wrapped as an numpy ndarray.

    Raises:
        ValueError: ``repetitions`` is less than one or size of ``state`` is not
            a power of 2.
        IndexError: An index from ``indices`` is out of range, given the number
            of qubits corresponding to the state.
    """
    if repetitions < 0:
        raise ValueError('Number of repetitions cannot be negative. Was {}'
                         .format(repetitions))
    num_qubits = _validate_num_qubits(state)
    _validate_indices(num_qubits, indices)

    if repetitions == 0 or len(indices) == 0:
        return np.zeros(shape=(repetitions, len(indices)))

    # Calculate the measurement probabilities.
    probs = _probs(state, indices, num_qubits)

    # We now have the probability vector, correctly ordered, so sample over
    # it. Note that we us ints here, since numpy's choice does not allow for
    # choosing from a list of tuples or list of lists.
    result = np.random.choice(len(probs), size=repetitions, p=probs)
    # Convert to bools and rearrange to match repetition being the outer list.
    return np.transpose([(1 & (result >> i)).astype(np.bool) for i in
                         range(len(indices))])
Samples repeatedly from measurements in the computational basis.

    Note that this does not modify the passed in state.

    Args:
        state: The multi-qubit wavefunction to be sampled. This is an array of
            2 to the power of the number of qubit complex numbers, and so
            state must be of size ``2**integer``.  The state can be a vector of
            size ``2**integer`` or a tensor of shape ``(2, 2, ..., 2)``.
        indices: Which qubits are measured. The state is assumed to be supplied
            in big endian order. That is the xth index of v, when expressed as
            a bitstring, has its largest values in the 0th index.
        repetitions: The number of times to sample the state.

    Returns:
        Measurement results with True corresponding to the ``|1⟩`` state.
        The outer list is for repetitions, and the inner corresponds to
        measurements ordered by the supplied qubits. These lists
        are wrapped as an numpy ndarray.

    Raises:
        ValueError: ``repetitions`` is less than one or size of ``state`` is not
            a power of 2.
        IndexError: An index from ``indices`` is out of range, given the number
            of qubits corresponding to the state.