Instruction:
Can you tell what is the following Python 3 function doing
def update_backend_info(self, interval=60):
    my_thread = threading.currentThread()
    current_interval = 0
    started = False
    all_dead = False
    stati = [None]*len(self._backends)
    while getattr(my_thread, "do_run", True) and not all_dead:
        if current_interval == interval or started is False:
            for ind, back in enumerate(self._backends):
                _value = self.children[ind].children[2].value
                _head = _value.split('')[0]
                try:
                    _status = back.status()
                    stati[ind] = _status
                except Exception:  # pylint: disable=W0703
                    self.children[ind].children[2].value = _value.replace(
                        _head, "
")
                    self.children[ind]._is_alive = False
                else:
                    self.children[ind]._is_alive = True
                    self.children[ind].children[2].value = _value.replace(
                        _head, "
")

            idx = list(range(len(self._backends)))
            pending = [s.pending_jobs for s in stati]
            _, least_idx = zip(*sorted(zip(pending, idx)))

            # Make sure least pending is operational
            for ind in least_idx:
                if stati[ind].operational:
                    least_pending_idx = ind
                    break

            for var in idx:
                if var == least_pending_idx:
                    self.children[var].children[4].value = "
True
"
                else:
                    self.children[var].children[4].value = "
False
"

                self.children[var].children[3].children[1].value = pending[var]
                self.children[var].children[3].children[1].max = max(
                    self.children[var].children[3].children[1].max, pending[var]+10)
                if stati[var].operational:
                    self.children[var].children[5].value = "
True
"
                else:
                    self.children[var].children[5].value = "
False
"

            started = True
            current_interval = 0
        time.sleep(1)
        all_dead = not any([wid._is_alive for wid in self.children])
        current_interval += 1

Response: Updates the monitor info of the current thread. Called from another thread.


Instruction:
Can you create a Python 3 function that
generates a jobs_pending progress bar widget.

Response:
def generate_jobs_pending_widget():
    """Generates a jobs_pending progress bar widget.
    """
    pbar = widgets.IntProgress(
        value=0,
        min=0,
        max=50,
        description='',
        orientation='horizontal', layout=widgets.Layout(max_width='180px'))
    pbar.style.bar_color = '#71cddd'

    pbar_current = widgets.Label(
        value=str(pbar.value), layout=widgets.Layout(min_width='auto'))
    pbar_max = widgets.Label(
        value=str(pbar.max), layout=widgets.Layout(min_width='auto'))

    def _on_max_change(change):
        pbar_max.value = str(change['new'])

    def _on_val_change(change):
        pbar_current.value = str(change['new'])

    pbar.observe(_on_max_change, names='max')
    pbar.observe(_on_val_change, names='value')

    jobs_widget = widgets.HBox([pbar_current, pbar, pbar_max],
                               layout=widgets.Layout(max_width='250px',
                                                     min_width='250px',
                                                     justify_content='center'))

    return jobs_widget


Instruction:
Make a summary of the following Python 3 code
def run(self, dag):
        new_dag = DAGCircuit()

        if self.layout is None:
            # LegacySwap renames the register in the DAG and does not match the property set
            self.layout = Layout.generate_trivial_layout(*dag.qregs.values())

        for layer in dag.serial_layers():
            subdag = layer['graph']

            for cnot_node in subdag.named_nodes('cx', 'CX'):
                control = cnot_node.qargs[0]
                target = cnot_node.qargs[1]

                physical_q0 = self.layout[control]
                physical_q1 = self.layout[target]
                if self.coupling_map.distance(physical_q0, physical_q1) != 1:
                    raise TranspilerError('The circuit requires a connection between physical '
                                          'qubits %s and %s' % (physical_q0, physical_q1))

                if (physical_q0, physical_q1) not in self.coupling_map.get_edges():
                    # A flip needs to be done

                    # Create the involved registers
                    if control[0] not in subdag.qregs.values():
                        subdag.add_qreg(control[0])
                    if target[0] not in subdag.qregs.values():
                        subdag.add_qreg(target[0])

                    # Add H gates around
                    subdag.apply_operation_back(HGate(), [target], [])
                    subdag.apply_operation_back(HGate(), [control], [])
                    subdag.apply_operation_front(HGate(), [target], [])
                    subdag.apply_operation_front(HGate(), [control], [])

                    # Flips the CX
                    cnot_node.qargs[0], cnot_node.qargs[1] = target, control

            new_dag.extend_back(subdag)

        return new_dag

Response: Flips the coupling map to match the directed coupling map.


Instruction:
Create a Python 3 function to
run one pass of cx cancellation on the circuit

Response:
def run(self, dag):
        """
        Run one pass of cx cancellation on the circuit

        Args:
            dag (DAGCircuit): the directed acyclic graph to run on.
        Returns:
            DAGCircuit: Transformed DAG.
        """
        cx_runs = dag.collect_runs(["cx"])
        for cx_run in cx_runs:
            # Partition the cx_run into chunks with equal gate arguments
            partition = []
            chunk = []
            for i in range(len(cx_run) - 1):
                chunk.append(cx_run[i])

                qargs0 = cx_run[i].qargs
                qargs1 = cx_run[i + 1].qargs

                if qargs0 != qargs1:
                    partition.append(chunk)
                    chunk = []
            chunk.append(cx_run[-1])
            partition.append(chunk)
            # Simplify each chunk in the partition
            for chunk in partition:
                if len(chunk) % 2 == 0:
                    for n in chunk:
                        dag.remove_op_node(n)
                else:
                    for n in chunk[1:]:
                        dag.remove_op_node(n)
        return dag


Instruction:
Can you write a function in Python 3 where it
returns a single backend matching the specified criteria.

Response:
def get_backend(self, name=None, **kwargs):
        """Return a single backend matching the specified filtering.

        Args:
            name (str): name of the backend.
            **kwargs (dict): dict used for filtering.

        Returns:
            BaseBackend: a backend matching the filtering.

        Raises:
            QiskitBackendNotFoundError: if no backend could be found or
                more than one backend matches.
        """
        backends = self.backends(name, **kwargs)
        if len(backends) > 1:
            raise QiskitBackendNotFoundError('More than one backend matches the criteria')
        elif not backends:
            raise QiskitBackendNotFoundError('No backend matches the criteria')

        return backends[0]


Instruction:
Write a Python 3 script for
returning the shape for bipartite matrix

Response:
def _bipartite_shape(self):
        """Return the shape for bipartite matrix"""
        return (self._input_dim, self._output_dim, self._input_dim,
                self._output_dim)


Instruction:
Create a Python 3 function to
return the conjugate of the QuantumChannel.

Response:
def conjugate(self):
        """Return the conjugate of the QuantumChannel."""
        return Choi(np.conj(self._data), self.input_dims(), self.output_dims())


Instruction:
Write a Python 3 function for
returning the transpose of the QuantumChannel.

Response:
def transpose(self):
        """Return the transpose of the QuantumChannel."""
        # Make bipartite matrix
        d_in, d_out = self.dim
        data = np.reshape(self._data, (d_in, d_out, d_in, d_out))
        # Swap input and output indicies on bipartite matrix
        data = np.transpose(data, (1, 0, 3, 2))
        # Transpose channel has input and output dimensions swapped
        data = np.reshape(data, (d_in * d_out, d_in * d_out))
        return Choi(
            data, input_dims=self.output_dims(), output_dims=self.input_dims())


Instruction:
Can you generate a brief explanation for the following Python 3 code
def compose(self, other, qargs=None, front=False):
        if qargs is not None:
            return Choi(
                SuperOp(self).compose(other, qargs=qargs, front=front))

        # Convert to Choi matrix
        if not isinstance(other, Choi):
            other = Choi(other)
        # Check dimensions match up
        if front and self._input_dim != other._output_dim:
            raise QiskitError(
                'input_dim of self must match output_dim of other')
        if not front and self._output_dim != other._input_dim:
            raise QiskitError(
                'input_dim of other must match output_dim of self')

        if front:
            first = np.reshape(other._data, other._bipartite_shape)
            second = np.reshape(self._data, self._bipartite_shape)
            input_dim = other._input_dim
            input_dims = other.input_dims()
            output_dim = self._output_dim
            output_dims = self.output_dims()
        else:
            first = np.reshape(self._data, self._bipartite_shape)
            second = np.reshape(other._data, other._bipartite_shape)
            input_dim = self._input_dim
            input_dims = self.input_dims()
            output_dim = other._output_dim
            output_dims = other.output_dims()

        # Contract Choi matrices for composition
        data = np.reshape(
            np.einsum('iAjB,AkBl->ikjl', first, second),
            (input_dim * output_dim, input_dim * output_dim))
        return Choi(data, input_dims, output_dims)

Response: Returns the composition of self and other.


Instruction:
Here you have a function in Python 3, explain what it does
def power(self, n):
        if n > 0:
            return super().power(n)
        return Choi(SuperOp(self).power(n))

Response: The matrix power of the superoperator matrix.


Instruction:
Can you tell what is the following Python 3 function doing
def _evolve(self, state, qargs=None):
        # If subsystem evolution we use the SuperOp representation
        if qargs is not None:
            return SuperOp(self)._evolve(state, qargs)
        # Otherwise we compute full evolution directly
        state = self._format_state(state, density_matrix=True)
        if state.shape[0] != self._input_dim:
            raise QiskitError(
                "QuantumChannel input dimension is not equal to state dimension."
            )
        return np.einsum('AB,AiBj->ij', state,
                         np.reshape(self._data, self._bipartite_shape))

Response: Evolve a state by the QuantumChannel.


Instruction:
Explain what the following Python 3 code does
def _tensor_product(self, other, reverse=False):
        # Convert other to Choi
        if not isinstance(other, Choi):
            other = Choi(other)

        if reverse:
            input_dims = self.input_dims() + other.input_dims()
            output_dims = self.output_dims() + other.output_dims()
            data = _bipartite_tensor(
                other.data,
                self._data,
                shape1=other._bipartite_shape,
                shape2=self._bipartite_shape)
        else:
            input_dims = other.input_dims() + self.input_dims()
            output_dims = other.output_dims() + self.output_dims()
            data = _bipartite_tensor(
                self._data,
                other.data,
                shape1=self._bipartite_shape,
                shape2=other._bipartite_shape)
        return Choi(data, input_dims, output_dims)

Response: Returns the tensor product of two quantum channels.


Instruction:
Create a Python 3 function for
getting the number and size of unique registers from bit_labels list.

Response:
def _get_register_specs(bit_labels):
    """Get the number and size of unique registers from bit_labels list.

    Args:
        bit_labels (list): this list is of the form::

            [['reg1', 0], ['reg1', 1], ['reg2', 0]]

            which indicates a register named "reg1" of size 2
            and a register named "reg2" of size 1. This is the
            format of classic and quantum bit labels in qobj
            header.

    Yields:
        tuple: iterator of register_name:size pairs.
    """
    it = itertools.groupby(bit_labels, operator.itemgetter(0))
    for register_name, sub_it in it:
        yield register_name, max(ind[1] for ind in sub_it) + 1


Instruction:
Make a summary of the following Python 3 code
def _truncate_float(matchobj, format_str='0.2g'):
    if matchobj.group(0):
        return format(float(matchobj.group(0)), format_str)
    return ''

Response: Truncate long floats in the log file


Instruction:
Given the following Python 3 function, write the documentation
def latex(self, aliases=None):
        self._initialize_latex_array(aliases)
        self._build_latex_array(aliases)
        header_1 = r"""% \documentclass[preview]{standalone}
% If the image is too large to fit on this documentclass use
\documentclass[draft]{beamer}
"""
        beamer_line = "\\usepackage[size=custom,height=%d,width=%d,scale=%.1f]{beamerposter}\n"
        header_2 = r"""% instead and customize the height and width (in cm) to fit.
% Large images may run out of memory quickly.
% To fix this use the LuaLaTeX compiler, which dynamically
% allocates memory.
\usepackage[braket, qm]{qcircuit}
\usepackage{amsmath}
\pdfmapfile{+sansmathaccent.map}
% \usepackage[landscape]{geometry}
% Comment out the above line if using the beamer documentclass.
\begin{document}
\begin{equation*}"""
        qcircuit_line = r"""
    \Qcircuit @C=%.1fem @R=%.1fem @!R {
"""
        output = io.StringIO()
        output.write(header_1)
        output.write('%% img_width = %d, img_depth = %d\n' % (self.img_width, self.img_depth))
        output.write(beamer_line % self._get_beamer_page())
        output.write(header_2)
        output.write(qcircuit_line %
                     (self.column_separation, self.row_separation))
        for i in range(self.img_width):
            output.write("\t \t")
            for j in range(self.img_depth + 1):
                cell_str = self._latex[i][j]
                # Don't truncate offset float if drawing a barrier
                if 'barrier' in cell_str:
                    output.write(cell_str)
                else:
                    # floats can cause "Dimension too large" latex error in
                    # xymatrix this truncates floats to avoid issue.
                    cell_str = re.sub(r'[-+]?\d*\.\d{2,}|\d{2,}',
                                      _truncate_float,
                                      cell_str)
                    output.write(cell_str)
                if j != self.img_depth:
                    output.write(" & ")
                else:
                    output.write(r'\\' + '\n')
        output.write('\t }\n')
        output.write('\\end{equation*}\n\n')
        output.write('\\end{document}')
        contents = output.getvalue()
        output.close()
        return contents

Response: Return LaTeX representation of the circuit.


Instruction:
Create a Python 3 function for
getting the number of columns in the circuit.

Response:
def _get_image_depth(self):
        """Get depth information for the circuit.

        Returns:
            int: number of columns in the circuit
            int: total size of columns in the circuit
        """

        max_column_widths = []

        for layer in self.ops:

            # store the max width for the layer
            current_max = 0

            for op in layer:

                # update current op width
                arg_str_len = 0

                # the wide gates
                for arg in op.op.params:
                    arg_str = re.sub(r'[-+]?\d*\.\d{2,}|\d{2,}',
                                     _truncate_float, str(arg))
                    arg_str_len += len(arg_str)

                # the width of the column is the max of all the gates in the column
                current_max = max(arg_str_len, current_max)

            max_column_widths.append(current_max)

        # wires in the beginning and end
        columns = 2
        # each layer is one column
        columns += len(self.ops)

        # every 3 characters is roughly one extra 'unit' of width in the cell
        # the gate name is 1 extra 'unit'
        # the qubit/cbit labels plus initial states is 2 more
        # the wires poking out at the ends is 2 more
        sum_column_widths = sum(1 + v / 3 for v in max_column_widths)

        # could be a fraction so ceil
        return columns, math.ceil(sum_column_widths) + 4


Instruction:
How would you explain what the following Python 3 function does
def _get_beamer_page(self):
        # PIL python package limits image size to around a quarter gigabyte
        # this means the beamer image should be limited to < 50000
        # if you want to avoid a "warning" too, set it to < 25000
        PIL_limit = 40000

        # the beamer latex template limits each dimension to < 19 feet
        # (i.e. 575cm)
        beamer_limit = 550

        # columns are roughly twice as big as rows
        aspect_ratio = self.sum_row_heights / self.sum_column_widths

        # choose a page margin so circuit is not cropped
        margin_factor = 1.5
        height = min(self.sum_row_heights * margin_factor, beamer_limit)
        width = min(self.sum_column_widths * margin_factor, beamer_limit)

        # if too large, make it fit
        if height * width > PIL_limit:
            height = min(np.sqrt(PIL_limit * aspect_ratio), beamer_limit)
            width = min(np.sqrt(PIL_limit / aspect_ratio), beamer_limit)

        # if too small, give it a minimum size
        height = max(height, 10)
        width = max(width, 10)

        return (height, width, self.scale)

Response: Get height width and scale attributes for the beamer page.


Instruction:
Create a Python 3 function to
build a LaTeX string for the current qubit names.

Response:
def _build_latex_array(self, aliases=None):
        """Returns an array of strings containing \\LaTeX for this circuit.

        If aliases is not None, aliases contains a dict mapping
        the current qubits in the circuit to new qubit names.
        We will deduce the register names and sizes from aliases.
        """
        columns = 1

        # Rename qregs if necessary
        if aliases:
            qregdata = {}
            for q in aliases.values():
                if q[0] not in qregdata:
                    qregdata[q[0]] = q[1] + 1
                elif qregdata[q[0]] < q[1] + 1:
                    qregdata[q[0]] = q[1] + 1
        else:
            qregdata = self.qregs

        for column, layer in enumerate(self.ops, 1):
            for op in layer:
                if op.condition:
                    mask = self._get_mask(op.condition[0])
                    cl_reg = self.clbit_list[self._ffs(mask)]
                    if_reg = cl_reg[0]
                    pos_2 = self.img_regs[cl_reg]
                    if_value = format(op.condition[1],
                                      'b').zfill(self.cregs[if_reg])[::-1]
                if op.name not in ['measure', 'barrier', 'snapshot', 'load',
                                   'save', 'noise']:
                    nm = op.name
                    qarglist = op.qargs
                    if aliases is not None:
                        qarglist = map(lambda x: aliases[x], qarglist)
                    if len(qarglist) == 1:
                        pos_1 = self.img_regs[(qarglist[0][0],
                                               qarglist[0][1])]

                        if op.condition:
                            mask = self._get_mask(op.condition[0])
                            cl_reg = self.clbit_list[self._ffs(mask)]
                            if_reg = cl_reg[0]
                            pos_2 = self.img_regs[cl_reg]

                            if nm == "x":
                                self._latex[pos_1][column] = "\\gate{X}"
                            elif nm == "y":
                                self._latex[pos_1][column] = "\\gate{Y}"
                            elif nm == "z":
                                self._latex[pos_1][column] = "\\gate{Z}"
                            elif nm == "h":
                                self._latex[pos_1][column] = "\\gate{H}"
                            elif nm == "s":
                                self._latex[pos_1][column] = "\\gate{S}"
                            elif nm == "sdg":
                                self._latex[pos_1][column] = "\\gate{S^\\dag}"
                            elif nm == "t":
                                self._latex[pos_1][column] = "\\gate{T}"
                            elif nm == "tdg":
                                self._latex[pos_1][column] = "\\gate{T^\\dag}"
                            elif nm == "u0":
                                self._latex[pos_1][column] = "\\gate{U_0(%s)}" % (
                                    op.op.params[0])
                            elif nm == "u1":
                                self._latex[pos_1][column] = "\\gate{U_1(%s)}" % (
                                    op.op.params[0])
                            elif nm == "u2":
                                self._latex[pos_1][column] = \
                                    "\\gate{U_2\\left(%s,%s\\right)}" % (
                                        op.op.params[0], op.op.params[1])
                            elif nm == "u3":
                                self._latex[pos_1][column] = ("\\gate{U_3(%s,%s,%s)}" % (
                                    op.op.params[0],
                                    op.op.params[1],
                                    op.op.params[2]))
                            elif nm == "rx":
                                self._latex[pos_1][column] = "\\gate{R_x(%s)}" % (
                                    op.op.params[0])
                            elif nm == "ry":
                                self._latex[pos_1][column] = "\\gate{R_y(%s)}" % (
                                    op.op.params[0])
                            elif nm == "rz":
                                self._latex[pos_1][column] = "\\gate{R_z(%s)}" % (
                                    op.op.params[0])
                            else:
                                self._latex[pos_1][columns] = "\\gate{%s}" % nm

                            gap = pos_2 - pos_1
                            for i in range(self.cregs[if_reg]):
                                if if_value[i] == '1':
                                    self._latex[pos_2 + i][column] = \
                                        "\\control \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1
                                else:
                                    self._latex[pos_2 + i][column] = \
                                        "\\controlo \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1

                        else:
                            if nm == "x":
                                self._latex[pos_1][column] = "\\gate{X}"
                            elif nm == "y":
                                self._latex[pos_1][column] = "\\gate{Y}"
                            elif nm == "z":
                                self._latex[pos_1][column] = "\\gate{Z}"
                            elif nm == "h":
                                self._latex[pos_1][column] = "\\gate{H}"
                            elif nm == "s":
                                self._latex[pos_1][column] = "\\gate{S}"
                            elif nm == "sdg":
                                self._latex[pos_1][column] = "\\gate{S^\\dag}"
                            elif nm == "t":
                                self._latex[pos_1][column] = "\\gate{T}"
                            elif nm == "tdg":
                                self._latex[pos_1][column] = "\\gate{T^\\dag}"
                            elif nm == "u0":
                                self._latex[pos_1][column] = "\\gate{U_0(%s)}" % (
                                    op.op.params[0])
                            elif nm == "u1":
                                self._latex[pos_1][column] = "\\gate{U_1(%s)}" % (
                                    op.op.params[0])
                            elif nm == "u2":
                                self._latex[pos_1][column] = \
                                    "\\gate{U_2\\left(%s,%s\\right)}" % (
                                        op.op.params[0], op.op.params[1])
                            elif nm == "u3":
                                self._latex[pos_1][column] = ("\\gate{U_3(%s,%s,%s)}" % (
                                    op.op.params[0],
                                    op.op.params[1],
                                    op.op.params[2]))
                            elif nm == "rx":
                                self._latex[pos_1][column] = "\\gate{R_x(%s)}" % (
                                    op.op.params[0])
                            elif nm == "ry":
                                self._latex[pos_1][column] = "\\gate{R_y(%s)}" % (
                                    op.op.params[0])
                            elif nm == "rz":
                                self._latex[pos_1][column] = "\\gate{R_z(%s)}" % (
                                    op.op.params[0])
                            elif nm == "reset":
                                self._latex[pos_1][column] = (
                                    "\\push{\\rule{.6em}{0em}\\ket{0}\\"
                                    "rule{.2em}{0em}} \\qw")
                            else:
                                self._latex[pos_1][columns] = "\\gate{%s}" % nm

                    elif len(qarglist) == 2:
                        pos_1 = self.img_regs[(qarglist[0][0], qarglist[0][1])]
                        pos_2 = self.img_regs[(qarglist[1][0], qarglist[1][1])]

                        if op.condition:
                            pos_3 = self.img_regs[(if_reg, 0)]
                            temp = [pos_1, pos_2, pos_3]
                            temp.sort(key=int)
                            bottom = temp[1]

                            gap = pos_3 - bottom
                            for i in range(self.cregs[if_reg]):
                                if if_value[i] == '1':
                                    self._latex[pos_3 + i][column] = \
                                        "\\control \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1
                                else:
                                    self._latex[pos_3 + i][column] = \
                                        "\\controlo \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1

                            if nm == "cx":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\targ"
                            elif nm == "cz":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\control\\qw"
                            elif nm == "cy":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\gate{Y}"
                            elif nm == "ch":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\gate{H}"
                            elif nm == "swap":
                                self._latex[pos_1][column] = "\\qswap"
                                self._latex[pos_2][column] = \
                                    "\\qswap \\qwx[" + str(pos_1 - pos_2) + "]"
                            elif nm == "crz":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = \
                                    "\\gate{R_z(%s)}" % (op.op.params[0])
                            elif nm == "cu1":
                                self._latex[pos_1][column - 1] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column - 1] = "\\control\\qw"
                                self._latex[min(pos_1, pos_2)][column] = \
                                    "\\dstick{%s}\\qw" % (op.op.params[0])
                                self._latex[max(pos_1, pos_2)][column] = "\\qw"
                            elif nm == "cu3":
                                self._latex[pos_1][column] = \
                                    "\\ctrl{" + str(pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = \
                                    "\\gate{U_3(%s,%s,%s)}" % (op.op.params[0],
                                                               op.op.params[1],
                                                               op.op.params[2])
                        else:
                            temp = [pos_1, pos_2]
                            temp.sort(key=int)

                            if nm == "cx":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\targ"
                            elif nm == "cz":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\control\\qw"
                            elif nm == "cy":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\gate{Y}"
                            elif nm == "ch":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\gate{H}"
                            elif nm == "swap":
                                self._latex[pos_1][column] = "\\qswap"
                                self._latex[pos_2][column] = \
                                    "\\qswap \\qwx[" + str(pos_1 - pos_2) + "]"
                            elif nm == "crz":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = \
                                    "\\gate{R_z(%s)}" % (op.op.params[0])
                            elif nm == "cu1":
                                self._latex[pos_1][column - 1] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column - 1] = "\\control\\qw"
                                self._latex[min(pos_1, pos_2)][column] = \
                                    "\\dstick{%s}\\qw" % (op.op.params[0])
                                self._latex[max(pos_1, pos_2)][column] = "\\qw"
                            elif nm == "cu3":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = ("\\gate{U_3(%s,%s,%s)}" % (
                                    op.op.params[0],
                                    op.op.params[1],
                                    op.op.params[2]))
                            else:
                                start_pos = min([pos_1, pos_2])
                                stop_pos = max([pos_1, pos_2])
                                if stop_pos - start_pos >= 2:
                                    delta = stop_pos - start_pos
                                    self._latex[start_pos][columns] = (
                                        "\\multigate{%s}{%s}" % (delta, nm))
                                    for i_pos in range(start_pos + 1, stop_pos + 1):
                                        self._latex[i_pos][columns] = "\\ghost{%s}" % nm
                                else:
                                    self._latex[start_pos][columns] = (
                                        "\\multigate{1}{%s}" % nm)
                                    self._latex[stop_pos][columns] = "\\ghost{%s}" % nm

                    elif len(qarglist) == 3:
                        pos_1 = self.img_regs[(qarglist[0][0], qarglist[0][1])]
                        pos_2 = self.img_regs[(qarglist[1][0], qarglist[1][1])]
                        pos_3 = self.img_regs[(qarglist[2][0], qarglist[2][1])]

                        if op.condition:
                            pos_4 = self.img_regs[(if_reg, 0)]

                            temp = [pos_1, pos_2, pos_3, pos_4]
                            temp.sort(key=int)
                            bottom = temp[2]

                            prev_column = [x[column - 1] for x in self._latex]
                            for item, prev_entry in enumerate(prev_column):
                                if 'barrier' in prev_entry:
                                    span = re.search('barrier{(.*)}', prev_entry)
                                    if span and any(i in temp for i in range(
                                            item, int(span.group(1)))):
                                        self._latex[item][column - 1] = \
                                            prev_entry.replace(
                                                '\\barrier{',
                                                '\\barrier[-0.65em]{')

                            gap = pos_4 - bottom
                            for i in range(self.cregs[if_reg]):
                                if if_value[i] == '1':
                                    self._latex[pos_4 + i][column] = \
                                        "\\control \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1
                                else:
                                    self._latex[pos_4 + i][column] = \
                                        "\\controlo \\cw \\cwx[-" + str(gap) + "]"
                                    gap = 1

                            if nm == "ccx":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\ctrl{" + str(
                                    pos_3 - pos_2) + "}"
                                self._latex[pos_3][column] = "\\targ"

                            if nm == "cswap":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\qswap"
                                self._latex[pos_3][column] = \
                                    "\\qswap \\qwx[" + str(pos_2 - pos_3) + "]"
                        else:
                            temp = [pos_1, pos_2, pos_3]
                            temp.sort(key=int)

                            prev_column = [x[column - 1] for x in self._latex]
                            for item, prev_entry in enumerate(prev_column):
                                if 'barrier' in prev_entry:
                                    span = re.search('barrier{(.*)}', prev_entry)
                                    if span and any(i in temp for i in range(
                                            item, int(span.group(1)))):
                                        self._latex[item][column - 1] = \
                                            prev_entry.replace(
                                                '\\barrier{',
                                                '\\barrier[-0.65em]{')

                            if nm == "ccx":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\ctrl{" + str(
                                    pos_3 - pos_2) + "}"
                                self._latex[pos_3][column] = "\\targ"

                            elif nm == "cswap":
                                self._latex[pos_1][column] = "\\ctrl{" + str(
                                    pos_2 - pos_1) + "}"
                                self._latex[pos_2][column] = "\\qswap"
                                self._latex[pos_3][column] = \
                                    "\\qswap \\qwx[" + str(pos_2 - pos_3) + "]"
                            else:
                                start_pos = min([pos_1, pos_2, pos_3])
                                stop_pos = max([pos_1, pos_2, pos_3])
                                if stop_pos - start_pos >= 3:
                                    delta = stop_pos - start_pos
                                    self._latex[start_pos][columns] = (
                                        "\\multigate{%s}{%s}" % (delta, nm))
                                    for i_pos in range(start_pos + 1, stop_pos + 1):
                                        self._latex[i_pos][columns] = "\\ghost{%s}" % nm
                                else:
                                    self._latex[pos_1][columns] = (
                                        "\\multigate{2}{%s}" % nm)
                                    self._latex[pos_2][columns] = "\\ghost{%s}" % nm
                                    self._latex[pos_3][columns] = "\\ghost{%s}" % nm

                    elif len(qarglist) > 3:
                        nbits = len(qarglist)
                        pos_array = [self.img_regs[(qarglist[0][0],
                                                    qarglist[0][1])]]
                        for i in range(1, nbits):
                            pos_array.append(self.img_regs[(qarglist[i][0],
                                                            qarglist[i][1])])
                        pos_start = min(pos_array)
                        pos_stop = max(pos_array)
                        delta = pos_stop - pos_start
                        self._latex[pos_start][columns] = (
                            "\\multigate{%s}{%s}" % (nbits - 1, nm))
                        for pos in range(pos_start + 1, pos_stop + 1):
                            self._latex[pos][columns] = "\\ghost{%s}" % nm

                elif op.name == "measure":
                    if (len(op.cargs) != 1
                            or len(op.qargs) != 1
                            or op.op.params):
                        raise exceptions.VisualizationError("bad operation record")

                    if op.condition:
                        raise exceptions.VisualizationError(
                            "If controlled measures currently not supported.")

                    qname, qindex = op.qargs[0]
                    cname, cindex = op.cargs[0]
                    if aliases:
                        newq = aliases[(qname, qindex)]
                        qname = newq[0]
                        qindex = newq[1]

                    pos_1 = self.img_regs[(qname, qindex)]
                    pos_2 = self.img_regs[(cname, cindex)]

                    try:
                        self._latex[pos_1][column] = "\\meter"
                        prev_column = [x[column - 1] for x in self._latex]
                        for item, prev_entry in enumerate(prev_column):
                            if 'barrier' in prev_entry:
                                span = re.search('barrier{(.*)}', prev_entry)
                                if span and (
                                        item + int(span.group(1))) - pos_1 >= 0:
                                    self._latex[item][column - 1] = \
                                        prev_entry.replace(
                                            '\\barrier{',
                                            '\\barrier[-1.15em]{')

                        self._latex[pos_2][column] = \
                            "\\cw \\cwx[-" + str(pos_2 - pos_1) + "]"
                    except Exception as e:
                        raise exceptions.VisualizationError(
                            'Error during Latex building: %s' % str(e))

                elif op.name in ['barrier', 'snapshot', 'load', 'save',
                                 'noise']:
                    if self.plot_barriers:
                        qarglist = op.qargs
                        indexes = [self._get_qubit_index(x) for x in qarglist]
                        start_bit = self.qubit_list[min(indexes)]
                        if aliases is not None:
                            qarglist = map(lambda x: aliases[x], qarglist)
                        start = self.img_regs[start_bit]
                        span = len(op.qargs) - 1

                        self._latex[start][column] = "\\qw \\barrier{" + str(
                            span) + "}"
                else:
                    raise exceptions.VisualizationError("bad node data")


Instruction:
Given the following Python 3 function, write the documentation
def _get_qubit_index(self, qubit):
        for i, bit in enumerate(self.qubit_list):
            if qubit == bit:
                qindex = i
                break
        else:
            raise exceptions.VisualizationError("unable to find bit for operation")
        return qindex

Response: Get the index number for a qubit in the bit list


Instruction:
Here you have a function in Python 3, explain what it does
def _load_schema(file_path, name=None):
    if name is None:
        # filename without extension
        name = os.path.splitext(os.path.basename(file_path))[0]
    if name not in _SCHEMAS:
        with open(file_path, 'r') as schema_file:
            _SCHEMAS[name] = json.load(schema_file)

    return _SCHEMAS[name]

Response: Loads the schema for use in future validations.


Instruction:
Can you write a function in Python 3 where it
generates and store a validator for the given name and schema.

Response:
def _get_validator(name, schema=None, check_schema=True,
                   validator_class=None, **validator_kwargs):
    """Generate validator for JSON schema.

    Args:
        name (str): Name for validator. Will be validator key in
            `_VALIDATORS` dict.
        schema (dict): JSON schema `dict`. If not provided searches for schema
            in `_SCHEMAS`.
        check_schema (bool): Verify schema is valid.
        validator_class (jsonschema.IValidator): jsonschema IValidator instance.
            Default behavior is to determine this from the schema `$schema`
            field.
        **validator_kwargs (dict): Additional keyword arguments for validator.

    Return:
        jsonschema.IValidator: Validator for JSON schema.

    Raises:
        SchemaValidationError: Raised if validation fails.
    """
    if schema is None:
        try:
            schema = _SCHEMAS[name]
        except KeyError:
            raise SchemaValidationError("Valid schema name or schema must "
                                        "be provided.")

    if name not in _VALIDATORS:

        # Resolve JSON spec from schema if needed
        if validator_class is None:
            validator_class = jsonschema.validators.validator_for(schema)

        # Generate and store validator in _VALIDATORS
        _VALIDATORS[name] = validator_class(schema, **validator_kwargs)

    validator = _VALIDATORS[name]

    if check_schema:
        validator.check_schema(schema)

    return validator


Instruction:
Can you write a function in Python 3 where it
loads all default schemas into _SCHEMAS and _VALIDATORS.

Response:
def _load_schemas_and_validators():
    """Load all default schemas into `_SCHEMAS`."""
    schema_base_path = os.path.join(os.path.dirname(__file__), '../..')
    for name, path in _DEFAULT_SCHEMA_PATHS.items():
        _load_schema(os.path.join(schema_base_path, path), name)
        _get_validator(name)


Instruction:
Can you tell what is the following Python 3 function doing
def validate_json_against_schema(json_dict, schema,
                                 err_msg=None):
    """Validates JSON dict against a schema.

    Args:
        json_dict (dict): JSON to be validated.
        schema (dict or str): JSON schema dictionary or the name of one of the
            standards schemas in Qiskit to validate against it. The list of
            standard schemas is: ``backend_configuration``,
            ``backend_properties``, ``backend_status``,
            ``default_pulse_configuration``, ``job_status``, ``qobj``,
            ``result``.
        err_msg (str): Optional error message.

    Raises:
        SchemaValidationError: Raised if validation fails.
    """

    try:
        if isinstance(schema, str):
            schema_name = schema
            schema = _SCHEMAS[schema_name]
            validator = _get_validator(schema_name)
            validator.validate(json_dict)
        else:
            jsonschema.validate(json_dict, schema)
    except jsonschema.ValidationError as err:
        if err_msg is None:
            err_msg = "JSON failed validation. Set Qiskit log level to DEBUG " \
                      "for further information."
        newerr = SchemaValidationError(err_msg)
        newerr.__cause__ = _SummaryValidationError(err)
        logger.debug('%s', _format_causes(err))
        raise newerr

Response: Validates a JSON dict against a given schema.


Instruction:
Create a Python 3 function for
returning a string representation of the validation error.

Response:
def _format_causes(err, level=0):
    """Return a cascading explanation of the validation error.

    Returns a cascading explanation of the validation error in the form of::

        <validator> failed @ <subfield_path> because of:
            <validator> failed @ <subfield_path> because of:
                ...
            <validator> failed @ <subfield_path> because of:
                ...
            ...

    For example::

        'oneOf' failed @ '<root>' because of:
            'required' failed @ '<root>.config' because of:
                'meas_level' is a required property

    Meaning the validator 'oneOf' failed while validating the whole object
    because of the validator 'required' failing while validating the property
    'config' because its 'meas_level' field is missing.

    The cascade repeats the format "<validator> failed @ <path> because of"
    until there are no deeper causes. In this case, the string representation
    of the error is shown.

    Args:
        err (jsonschema.ValidationError): the instance to explain.
        level (int): starting level of indentation for the cascade of
            explanations.

    Return:
        str: a formatted string with the explanation of the error.

    """
    lines = []

    def _print(string, offset=0):
        lines.append(_pad(string, offset=offset))

    def _pad(string, offset=0):
        padding = '  ' * (level + offset)
        padded_lines = [padding + line for line in string.split('\n')]
        return '\n'.join(padded_lines)

    def _format_path(path):
        def _format(item):
            if isinstance(item, str):
                return '.{}'.format(item)

            return '[{}]'.format(item)

        return ''.join(['<root>'] + list(map(_format, path)))

    _print('\'{}\' failed @ \'{}\' because of:'.format(
        err.validator, _format_path(err.absolute_path)))

    if not err.context:
        _print(str(err.message), offset=1)
    else:
        for suberr in err.context:
            lines.append(_format_causes(suberr, level+1))

    return '\n'.join(lines)


Instruction:
Write a Python 3 function that can
return the corresponding OPENQASM string.

Response:
def qasm(self, prec=15):
        """Return the corresponding OPENQASM string."""
        return ",".join([self.children[j].qasm(prec)
                         for j in range(self.size())])


Instruction:
Can you create a Python 3 function that
draws a quantum circuit to a single page of text.

Response:
def circuit_drawer(circuit,
                   scale=0.7,
                   filename=None,
                   style=None,
                   output='text',
                   interactive=False,
                   line_length=None,
                   plot_barriers=True,
                   reverse_bits=False,
                   justify=None):
    """Draw a quantum circuit to different formats (set by output parameter):
    0. text: ASCII art TextDrawing that can be printed in the console.
    1. latex: high-quality images, but heavy external software dependencies
    2. matplotlib: purely in Python with no external dependencies

    Args:
        circuit (QuantumCircuit): the quantum circuit to draw
        scale (float): scale of image to draw (shrink if < 1)
        filename (str): file path to save image to
        style (dict or str): dictionary of style or file name of style file.
            This option is only used by the `mpl`, `latex`, and `latex_source`
            output types. If a str is passed in that is the path to a json
            file which contains that will be open, parsed, and then used just
            as the input dict.
        output (TextDrawing): Select the output method to use for drawing the circuit.
            Valid choices are `text`, `latex`, `latex_source`, `mpl`. Note if
            one is not specified it will use latex and if that fails fallback
            to mpl. However this behavior is deprecated and in a future release
            the default will change.
        interactive (bool): when set true show the circuit in a new window
            (for `mpl` this depends on the matplotlib backend being used
            supporting this). Note when used with either the `text` or the
            `latex_source` output type this has no effect and will be silently
            ignored.
        line_length (int): Sets the length of the lines generated by `text`
            output type. This useful when the drawing does not fit in the
            console. If None (default), it will try to guess the console width
            using shutil.get_terminal_size(). However, if you're running in
            jupyter the default line length is set to 80 characters. If you
            don't want pagination at all, set `line_length=-1`.
        reverse_bits (bool): When set to True reverse the bit order inside
            registers for the output visualization.
        plot_barriers (bool): Enable/disable drawing barriers in the output
            circuit. Defaults to True.
        justify (string): Options are `left`, `right` or `none`, if anything
            else is supplied it defaults to left justified. It refers to where
            gates should be placed in the output circuit if there is an option.
            `none` results in each gate being placed in its own column. Currently
            only supported by text drawer.

    Returns:
        PIL.Image: (output `latex`) an in-memory representation of the image
            of the circuit diagram.
        matplotlib.figure: (output `mpl`) a matplotlib figure object for the
            circuit diagram.
        String: (output `latex_source`). The LaTeX source code.
        TextDrawing: (output `text`). A drawing that can be printed as ascii art
    Raises:
        VisualizationError: when an invalid output method is selected
        ImportError: when the output methods requieres non-installed libraries.

    .. _style-dict-doc:

    The style dict kwarg contains numerous options that define the style of the
    output circuit visualization. While the style dict is used by the `mpl`,
    `latex`, and `latex_source` outputs some options in that are only used
    by the `mpl` output. These options are defined below, if it is only used by
    the `mpl` output it is marked as such:

        textcolor (str): The color code to use for text. Defaults to
            `'#000000'` (`mpl` only)
        subtextcolor (str): The color code to use for subtext. Defaults to
            `'#000000'` (`mpl` only)
        linecolor (str): The color code to use for lines. Defaults to
            `'#000000'` (`mpl` only)
        creglinecolor (str): The color code to use for classical register lines
            `'#778899'`(`mpl` only)
        gatetextcolor (str): The color code to use for gate text `'#000000'`
            (`mpl` only)
        gatefacecolor (str): The color code to use for gates. Defaults to
            `'#ffffff'` (`mpl` only)
        barrierfacecolor (str): The color code to use for barriers. Defaults to
            `'#bdbdbd'` (`mpl` only)
        backgroundcolor (str): The color code to use for the background.
            Defaults to `'#ffffff'` (`mpl` only)
        fontsize (int): The font size to use for text. Defaults to 13 (`mpl`
            only)
        subfontsize (int): The font size to use for subtext. Defaults to 8
            (`mpl` only)
        displaytext (dict): A dictionary of the text to use for each element
            type in the output visualization. The default values are:
            {
                'id': 'id',
                'u0': 'U_0',
                'u1': 'U_1',
                'u2': 'U_2',
                'u3': 'U_3',
                'x': 'X',
                'y': 'Y',
                'z': 'Z',
                'h': 'H',
                's': 'S',
                'sdg': 'S^\\dagger',
                't': 'T',
                'tdg': 'T^\\dagger',
                'rx': 'R_x',
                'ry': 'R_y',
                'rz': 'R_z',
                'reset': '\\left|0\\right\\rangle'
            }
            You must specify all the necessary values if using this. There is
            no provision for passing an incomplete dict in. (`mpl` only)
        displaycolor (dict): The color codes to use for each circuit element.
            By default all values default to the value of `gatefacecolor` and
            the keys are the same as `displaytext`. Also, just like
            `displaytext` there is no provision for an incomplete dict passed
            in. (`mpl` only)
        latexdrawerstyle (bool): When set to True enable latex mode which will
            draw gates like the `latex` output modes. (`mpl` only)
        usepiformat (bool): When set to True use radians for output (`mpl`
            only)
        fold (int): The number of circuit elements to fold the circuit at.
            Defaults to 20 (`mpl` only)
        cregbundle (bool): If set True bundle classical registers (`mpl` only)
        showindex (bool): If set True draw an index. (`mpl` only)
        compress (bool): If set True draw a compressed circuit (`mpl` only)
        figwidth (int): The maximum width (in inches) for the output figure.
            (`mpl` only)
        dpi (int): The DPI to use for the output image. Defaults to 150 (`mpl`
            only)
        margin (list): `mpl` only
        creglinestyle (str): The style of line to use for classical registers.
            Choices are `'solid'`, `'doublet'`, or any valid matplotlib
            `linestyle` kwarg value. Defaults to `doublet`(`mpl` only)
    """
    image = None

    if output == 'text':
        return _text_circuit_drawer(circuit, filename=filename,
                                    line_length=line_length,
                                    reverse_bits=reverse_bits,
                                    plotbarriers=plot_barriers,
                                    justify=justify)
    elif output == 'latex':
        image = _latex_circuit_drawer(circuit, scale=scale,
                                      filename=filename, style=style,
                                      plot_barriers=plot_barriers,
                                      reverse_bits=reverse_bits,
                                      justify=justify)
    elif output == 'latex_source':
        return _generate_latex_source(circuit,
                                      filename=filename, scale=scale,
                                      style=style,
                                      plot_barriers=plot_barriers,
                                      reverse_bits=reverse_bits,
                                      justify=justify)
    elif output == 'mpl':
        image = _matplotlib_circuit_drawer(circuit, scale=scale,
                                           filename=filename, style=style,
                                           plot_barriers=plot_barriers,
                                           reverse_bits=reverse_bits,
                                           justify=justify)
    else:
        raise exceptions.VisualizationError(
            'Invalid output type %s selected. The only valid choices '
            'are latex, latex_source, text, and mpl' % output)
    if image and interactive:
        image.show()
    return image


Instruction:
How would you implement a function in Python 3 that
returns a TextDrawing instance that draws a single entry in ascii art.

Response:
def _text_circuit_drawer(circuit, filename=None, line_length=None, reverse_bits=False,
                         plotbarriers=True, justify=None, vertically_compressed=True):
    """
    Draws a circuit using ascii art.
    Args:
        circuit (QuantumCircuit): Input circuit
        filename (str): optional filename to write the result
        line_length (int): Optional. Breaks the circuit drawing to this length. This
                   useful when the drawing does not fit in the console. If
                   None (default), it will try to guess the console width using
                   shutil.get_terminal_size(). If you don't want pagination
                   at all, set line_length=-1.
        reverse_bits (bool): Rearrange the bits in reverse order.
        plotbarriers (bool): Draws the barriers when they are there.
        justify (str) : `left`, `right` or `none`. Defaults to `left`. Says how
                        the circuit should be justified.
        vertically_compressed (bool): Default is `True`. It merges the lines so the
                                      drawing will take less vertical room.
    Returns:
        TextDrawing: An instances that, when printed, draws the circuit in ascii art.
    """
    qregs, cregs, ops = utils._get_layered_instructions(circuit,
                                                        reverse_bits=reverse_bits,
                                                        justify=justify)
    text_drawing = _text.TextDrawing(qregs, cregs, ops)
    text_drawing.plotbarriers = plotbarriers
    text_drawing.line_length = line_length
    text_drawing.vertically_compressed = vertically_compressed

    if filename:
        text_drawing.dump(filename)
    return text_drawing


Instruction:
Can you tell what is the following Python 3 function doing
def _latex_circuit_drawer(circuit,
                          scale=0.7,
                          filename=None,
                          style=None,
                          plot_barriers=True,
                          reverse_bits=False,
                          justify=None):
    """Draw a quantum circuit based on latex (Qcircuit package)

    Requires version >=2.6.0 of the qcircuit LaTeX package.

    Args:
        circuit (QuantumCircuit): a quantum circuit
        scale (float): scaling factor
        filename (str): file path to save image to
        style (dict or str): dictionary of style or file name of style file
        reverse_bits (bool): When set to True reverse the bit order inside
            registers for the output visualization.
        plot_barriers (bool): Enable/disable drawing barriers in the output
            circuit. Defaults to True.
        justify (str) : `left`, `right` or `none`. Defaults to `left`. Says how
                        the circuit should be justified.

    Returns:
        PIL.Image: an in-memory representation of the circuit diagram

    Raises:
        OSError: usually indicates that ```pdflatex``` or ```pdftocairo``` is
                 missing.
        CalledProcessError: usually points errors during diagram creation.
    """
    tmpfilename = 'circuit'
    with tempfile.TemporaryDirectory() as tmpdirname:
        tmppath = os.path.join(tmpdirname, tmpfilename + '.tex')
        _generate_latex_source(circuit, filename=tmppath,
                               scale=scale, style=style,
                               plot_barriers=plot_barriers,
                               reverse_bits=reverse_bits, justify=justify)
        image = None
        try:

            subprocess.run(["pdflatex", "-halt-on-error",
                            "-output-directory={}".format(tmpdirname),
                            "{}".format(tmpfilename + '.tex')],
                           stdout=subprocess.PIPE, stderr=subprocess.DEVNULL,
                           check=True)
        except OSError as ex:
            if ex.errno == errno.ENOENT:
                logger.warning('WARNING: Unable to compile latex. '
                               'Is `pdflatex` installed? '
                               'Skipping latex circuit drawing...')
            raise
        except subprocess.CalledProcessError as ex:
            with open('latex_error.log', 'wb') as error_file:
                error_file.write(ex.stdout)
            logger.warning('WARNING Unable to compile latex. '
                           'The output from the pdflatex command can '
                           'be found in latex_error.log')
            raise
        else:
            try:
                base = os.path.join(tmpdirname, tmpfilename)
                subprocess.run(["pdftocairo", "-singlefile", "-png", "-q",
                                base + '.pdf', base])
                image = Image.open(base + '.png')
                image = utils._trim(image)
                os.remove(base + '.png')
                if filename:
                    image.save(filename, 'PNG')
            except OSError as ex:
                if ex.errno == errno.ENOENT:
                    logger.warning('WARNING: Unable to convert pdf to image. '
                                   'Is `poppler` installed? '
                                   'Skipping circuit drawing...')
                raise
        return image

Response: Draw a quantum circuit using LaTeX.


Instruction:
Create a Python 3 function to
convert QuantumCircuit to LaTeX string.

Response:
def _generate_latex_source(circuit, filename=None,
                           scale=0.7, style=None, reverse_bits=False,
                           plot_barriers=True, justify=None):
    """Convert QuantumCircuit to LaTeX string.

    Args:
        circuit (QuantumCircuit): input circuit
        scale (float): image scaling
        filename (str): optional filename to write latex
        style (dict or str): dictionary of style or file name of style file
        reverse_bits (bool): When set to True reverse the bit order inside
            registers for the output visualization.
        plot_barriers (bool): Enable/disable drawing barriers in the output
            circuit. Defaults to True.
        justify (str) : `left`, `right` or `none`. Defaults to `left`. Says how
                        the circuit should be justified.

    Returns:
        str: Latex string appropriate for writing to file.
    """
    qregs, cregs, ops = utils._get_layered_instructions(circuit,
                                                        reverse_bits=reverse_bits,
                                                        justify=justify)

    qcimg = _latex.QCircuitImage(qregs, cregs, ops, scale, style=style,
                                 plot_barriers=plot_barriers,
                                 reverse_bits=reverse_bits)
    latex = qcimg.latex()
    if filename:
        with open(filename, 'w') as latex_file:
            latex_file.write(latex)

    return latex


Instruction:
How would you explain what the following Python 3 function does
def _matplotlib_circuit_drawer(circuit,
                               scale=0.7,
                               filename=None,
                               style=None,
                               plot_barriers=True,
                               reverse_bits=False,
                               justify=None):
    """Draw a quantum circuit based on matplotlib.
    If `%matplotlib inline` is invoked in a Jupyter notebook, it visualizes a circuit inline.
    We recommend `%config InlineBackend.figure_format = 'svg'` for the inline visualization.

    Args:
        circuit (QuantumCircuit): a quantum circuit
        scale (float): scaling factor
        filename (str): file path to save image to
        style (dict or str): dictionary of style or file name of style file
        reverse_bits (bool): When set to True reverse the bit order inside
            registers for the output visualization.
        plot_barriers (bool): Enable/disable drawing barriers in the output
            circuit. Defaults to True.
        justify (str) : `left`, `right` or `none`. Defaults to `left`. Says how
            the circuit should be justified.

    Returns:
        matplotlib.figure: a matplotlib figure object for the circuit diagram
    """

    qregs, cregs, ops = utils._get_layered_instructions(circuit,
                                                        reverse_bits=reverse_bits,
                                                        justify=justify)
    qcd = _matplotlib.MatplotlibDrawer(qregs, cregs, ops, scale=scale, style=style,
                                       plot_barriers=plot_barriers,
                                       reverse_bits=reverse_bits)
    return qcd.draw(filename)

Response: Draw a single line of a quantum circuit using matplotlib.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def random_state(dim, seed=None):
    if seed is None:
        seed = np.random.randint(0, np.iinfo(np.int32).max)
    rng = np.random.RandomState(seed)
    # Random array over interval (0, 1]
    x = rng.rand(dim)
    x += x == 0
    x = -np.log(x)
    sumx = sum(x)
    phases = rng.rand(dim)*2.0*np.pi
    return np.sqrt(x/sumx)*np.exp(1j*phases)

Response:
Returns a random quantum state from the uniform Haar measure on
    state space.


Instruction:
Given the following Python 3 function, write the documentation
def random_unitary(dim, seed=None):
    if dim == 0 or not math.log2(dim).is_integer():
        raise QiskitError("Desired unitary dimension not a positive power of 2.")
    matrix = np.zeros([dim, dim], dtype=complex)
    for j in range(dim):
        if j == 0:
            a = random_state(dim, seed)
        else:
            a = random_state(dim)
        matrix[:, j] = np.copy(a)
        # Grahm-Schmidt Orthogonalize
        i = j-1
        while i >= 0:
            dc = np.vdot(matrix[:, i], a)
            matrix[:, j] = matrix[:, j]-dc*matrix[:, i]
            i = i - 1
        # normalize
        matrix[:, j] = matrix[:, j] * (1.0 / np.sqrt(np.vdot(matrix[:, j], matrix[:, j])))
    return Operator(matrix)

Response: Returns a random dim x dim unitary operator from the Haar measure.


Instruction:
How would you code a function in Python 3 to
generate a random density matrix rho.

Response:
def random_density_matrix(length, rank=None, method='Hilbert-Schmidt', seed=None):
    """
    Generate a random density matrix rho.

    Args:
        length (int): the length of the density matrix.
        rank (int or None): the rank of the density matrix. The default
            value is full-rank.
        method (string): the method to use.
            'Hilbert-Schmidt': sample rho from the Hilbert-Schmidt metric.
            'Bures': sample rho from the Bures metric.
        seed (int): Optional. To set a random seed.
    Returns:
        ndarray: rho (length, length) a density matrix.
    Raises:
        QiskitError: if the method is not valid.
    """
    if method == 'Hilbert-Schmidt':
        return __random_density_hs(length, rank, seed)
    elif method == 'Bures':
        return __random_density_bures(length, rank, seed)
    else:
        raise QiskitError('Error: unrecognized method {}'.format(method))


Instruction:
Implement a Python 3 function for
returning a normally distributed complex random matrix.

Response:
def __ginibre_matrix(nrow, ncol=None, seed=None):
    """
    Return a normally distributed complex random matrix.

    Args:
        nrow (int): number of rows in output matrix.
        ncol (int): number of columns in output matrix.
        seed (int): Optional. To set a random seed.
    Returns:
        ndarray: A complex rectangular matrix where each real and imaginary
            entry is sampled from the normal distribution.
    """
    if ncol is None:
        ncol = nrow
    if seed is not None:
        np.random.seed(seed)
    G = np.random.normal(size=(nrow, ncol)) + \
        np.random.normal(size=(nrow, ncol)) * 1j
    return G


Instruction:
Can you create a Python 3 function that
generates a random density matrix from the Hilbert - Schmidt metric.

Response:
def __random_density_hs(N, rank=None, seed=None):
    """
    Generate a random density matrix from the Hilbert-Schmidt metric.

    Args:
        N (int): the length of the density matrix.
        rank (int or None): the rank of the density matrix. The default
            value is full-rank.
        seed (int): Optional. To set a random seed.
    Returns:
        ndarray: rho (N,N  a density matrix.
    """
    G = __ginibre_matrix(N, rank, seed)
    G = G.dot(G.conj().T)
    return G / np.trace(G)


Instruction:
Make a summary of the following Python 3 code
def __random_density_bures(N, rank=None, seed=None):
    P = np.eye(N) + random_unitary(N).data
    G = P.dot(__ginibre_matrix(N, rank, seed))
    G = G.dot(G.conj().T)
    return G / np.trace(G)

Response: Generate a random density matrix from the Bures metric.


Instruction:
Make a summary of the following Python 3 code
def qasm(self, prec=15):
        string = ""
        for children in self.children:
            string += "  " + children.qasm(prec) + "\n"
        return string

Response: Return the corresponding OPENQASM string.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calls(self):
        lst = []
        for children in self.children:
            if children.type == "custom_unitary":
                lst.append(children.name)
        return lst

Response: Return a list of custom gate names in this gate body.


Instruction:
Can you implement a function in Python 3 that
returns the corresponding OPENQASM string.

Response:
def qasm(self, prec=15):
        """Return the corresponding OPENQASM string."""
        if self.value == pi:
            return "pi"

        return ccode(self.value, precision=prec)


Instruction:
Explain what the following Python 3 code does
def _define(self):
        definition = []
        q = QuantumRegister(1, "q")
        rule = [
            (U1Gate(-pi/2), [q[0]], [])
        ]
        for inst in rule:
            definition.append(inst)
        self.definition = definition

Response: Define the internal state of the class.


Instruction:
Can you generate the documentation for the following Python 3 function
def compose(self, other, qargs=None, front=False):
        if qargs is not None:
            return Chi(
                SuperOp(self).compose(other, qargs=qargs, front=front))

        # Convert other to Choi since we convert via Choi
        if not isinstance(other, Choi):
            other = Choi(other)
        # Check dimensions match up
        if front and self._input_dim != other._output_dim:
            raise QiskitError(
                'input_dim of self must match output_dim of other')
        if not front and self._output_dim != other._input_dim:
            raise QiskitError(
                'input_dim of other must match output_dim of self')
        # Since we cannot directly add two channels in the Chi
        # representation we convert to the Choi representation
        return Chi(Choi(self).compose(other, front=front))

Response: Return the composition of self and other.


Instruction:
Given the following Python 3 function, write the documentation
def power(self, n):
        if n > 0:
            return super().power(n)
        return Chi(SuperOp(self).power(n))

Response: The matrix power of the superoperator matrix.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def add(self, other):
        if not isinstance(other, Chi):
            other = Chi(other)
        if self.dim != other.dim:
            raise QiskitError("other QuantumChannel dimensions are not equal")
        return Chi(self._data + other.data, self._input_dims,
                   self._output_dims)

Response: Returns the linear addition of self and other.


Instruction:
Can you implement a function in Python 3 that
returns the QuantumChannel self + other.

Response:
def multiply(self, other):
        """Return the QuantumChannel self + other.

        Args:
            other (complex): a complex number.

        Returns:
            Chi: the scalar multiplication other * self as a Chi object.

        Raises:
            QiskitError: if other is not a valid scalar.
        """
        if not isinstance(other, Number):
            raise QiskitError("other is not a number")
        return Chi(other * self._data, self._input_dims, self._output_dims)


Instruction:
Explain what the following Python 3 code does
def _tensor_product(self, other, reverse=False):
        if not isinstance(other, Chi):
            other = Chi(other)
        if reverse:
            input_dims = self.input_dims() + other.input_dims()
            output_dims = self.output_dims() + other.output_dims()
            data = np.kron(other.data, self._data)
        else:
            input_dims = other.input_dims() + self.input_dims()
            output_dims = other.output_dims() + self.output_dims()
            data = np.kron(self._data, other.data)
        return Chi(data, input_dims, output_dims)

Response: Returns the tensor product of two quantum channels.


Instruction:
Here you have a function in Python 3, explain what it does
def conjugate(self):
        return SuperOp(
            np.conj(self._data), self.input_dims(), self.output_dims())

Response: Return the conjugate of the QuantumChannel.


Instruction:
Explain what the following Python 3 code does
def transpose(self):
        return SuperOp(
            np.transpose(self._data),
            input_dims=self.output_dims(),
            output_dims=self.input_dims())

Response: Return the transpose of the QuantumChannel.


Instruction:
Implement a function in Python 3 to
return the composition of self and other on the same subsystem.

Response:
def compose(self, other, qargs=None, front=False):
        """Return the composition channel self∘other.

        Args:
            other (QuantumChannel): a quantum channel.
            qargs (list): a list of subsystem positions to compose other on.
            front (bool): If False compose in standard order other(self(input))
                          otherwise compose in reverse order self(other(input))
                          [default: False]

        Returns:
            SuperOp: The composition channel as a SuperOp object.

        Raises:
            QiskitError: if other is not a QuantumChannel subclass, or
            has incompatible dimensions.
        """
        # Convert other to SuperOp
        if not isinstance(other, SuperOp):
            other = SuperOp(other)
        # Check dimensions are compatible
        if front and self.input_dims(qargs=qargs) != other.output_dims():
            raise QiskitError(
                'output_dims of other must match subsystem input_dims')
        if not front and self.output_dims(qargs=qargs) != other.input_dims():
            raise QiskitError(
                'input_dims of other must match subsystem output_dims')

        # Full composition of superoperators
        if qargs is None:
            if front:
                # Composition A(B(input))
                return SuperOp(
                    np.dot(self._data, other.data),
                    input_dims=other.input_dims(),
                    output_dims=self.output_dims())
            # Composition B(A(input))
            return SuperOp(
                np.dot(other.data, self._data),
                input_dims=self.input_dims(),
                output_dims=other.output_dims())
        # Composition on subsystem
        return self._compose_subsystem(other, qargs, front)


Instruction:
Can you tell what is the following Python 3 function doing
def power(self, n):
        if not isinstance(n, (int, np.integer)):
            raise QiskitError("Can only power with integer powers.")
        if self._input_dim != self._output_dim:
            raise QiskitError("Can only power with input_dim = output_dim.")
        # Override base class power so we can implement more efficiently
        # using Numpy.matrix_power
        return SuperOp(
            np.linalg.matrix_power(self._data, n), self.input_dims(),
            self.output_dims())

Response: Return the composition of a QuantumChannel with itself n times.


Instruction:
Explain what the following Python 3 code does
def add(self, other):
        # Convert other to SuperOp
        if not isinstance(other, SuperOp):
            other = SuperOp(other)
        if self.dim != other.dim:
            raise QiskitError("other QuantumChannel dimensions are not equal")
        return SuperOp(self._data + other.data, self.input_dims(),
                       self.output_dims())

Response: Return the QuantumChannel self + other.


Instruction:
Make a summary of the following Python 3 code
def multiply(self, other):
        if not isinstance(other, Number):
            raise QiskitError("other is not a number")
        return SuperOp(other * self._data, self.input_dims(),
                       self.output_dims())

Response: Return the QuantumChannel self + other.


Instruction:
How would you code a function in Python 3 to
evolve a state vector or density matrix by the QuantumChannel.

Response:
def _evolve(self, state, qargs=None):
        """Evolve a quantum state by the QuantumChannel.

        Args:
            state (QuantumState): The input statevector or density matrix.
            qargs (list): a list of QuantumState subsystem positions to apply
                           the operator on.

        Returns:
            DensityMatrix: the output quantum state as a density matrix.

        Raises:
            QiskitError: if the operator dimension does not match the
            specified QuantumState subsystem dimensions.
        """
        state = self._format_state(state, density_matrix=True)
        if qargs is None:
            if state.shape[0] != self._input_dim:
                raise QiskitError(
                    "QuantumChannel input dimension is not equal to state dimension."
                )
            shape_in = self._input_dim * self._input_dim
            shape_out = (self._output_dim, self._output_dim)
            # Return evolved density matrix
            return np.reshape(
                np.dot(self._data, np.reshape(state, shape_in, order='F')),
                shape_out,
                order='F')
        # Subsystem evolution
        return self._evolve_subsystem(state, qargs)


Instruction:
Create a Python 3 function to
return the composition channel of the subsystem.

Response:
def _compose_subsystem(self, other, qargs, front=False):
        """Return the composition channel."""
        # Compute tensor contraction indices from qargs
        input_dims = list(self.input_dims())
        output_dims = list(self.output_dims())
        if front:
            num_indices = len(self.input_dims())
            shift = 2 * len(self.output_dims())
            right_mul = True
            for pos, qubit in enumerate(qargs):
                input_dims[qubit] = other._input_dims[pos]
        else:
            num_indices = len(self.output_dims())
            shift = 0
            right_mul = False
            for pos, qubit in enumerate(qargs):
                output_dims[qubit] = other._output_dims[pos]
        # Reshape current matrix
        # Note that we must reverse the subsystem dimension order as
        # qubit 0 corresponds to the right-most position in the tensor
        # product, which is the last tensor wire index.
        tensor = np.reshape(self.data, self._shape)
        mat = np.reshape(other.data, other._shape)
        # Add first set of indicies
        indices = [2 * num_indices - 1 - qubit for qubit in qargs
                   ] + [num_indices - 1 - qubit for qubit in qargs]
        final_shape = [np.product(output_dims)**2, np.product(input_dims)**2]
        data = np.reshape(
            self._einsum_matmul(tensor, mat, indices, shift, right_mul),
            final_shape)
        return SuperOp(data, input_dims, output_dims)


Instruction:
Write a Python 3 script for
evolving a state by the operator on the subsystem.

Response:
def _evolve_subsystem(self, state, qargs):
        """Evolve a quantum state by the operator.

        Args:
            state (QuantumState): The input statevector or density matrix.
            qargs (list): a list of QuantumState subsystem positions to apply
                           the operator on.

        Returns:
            QuantumState: the output quantum state.

        Raises:
            QiskitError: if the operator dimension does not match the
            specified QuantumState subsystem dimensions.
        """
        mat = np.reshape(self.data, self._shape)
        # Hack to assume state is a N-qubit state until a proper class for states
        # is in place
        state_size = len(state)
        state_dims = self._automatic_dims(None, state_size)
        if self.input_dims() != len(qargs) * (2, ):
            raise QiskitError(
                "Channel input dimensions are not compatible with state subsystem dimensions."
            )
        # Return evolved density matrix
        tensor = np.reshape(state, 2 * state_dims)
        num_inidices = len(state_dims)
        indices = [num_inidices - 1 - qubit for qubit in qargs
                   ] + [2 * num_inidices - 1 - qubit for qubit in qargs]
        tensor = self._einsum_matmul(tensor, mat, indices)
        return np.reshape(tensor, [state_size, state_size])


Instruction:
Write a Python 3 script for
converting a QuantumCircuit or Instruction to a SuperOp.

Response:
def _instruction_to_superop(cls, instruction):
        """Convert a QuantumCircuit or Instruction to a SuperOp."""
        # Convert circuit to an instruction
        if isinstance(instruction, QuantumCircuit):
            instruction = instruction.to_instruction()
        # Initialize an identity superoperator of the correct size
        # of the circuit
        op = SuperOp(np.eye(4 ** instruction.num_qubits))
        op._append_instruction(instruction)
        return op


Instruction:
Here you have a function in Python 3, explain what it does
def _append_instruction(self, obj, qargs=None):
        if isinstance(obj, Instruction):
            chan = None
            if obj.name == 'reset':
                # For superoperator evolution we can simulate a reset as
                # a non-unitary supeorperator matrix
                chan = SuperOp(
                    np.array([[1, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0],
                              [0, 0, 0, 0]]))
            if obj.name == 'kraus':
                kraus = obj.params
                dim = len(kraus[0])
                chan = SuperOp(_to_superop('Kraus', (kraus, None), dim, dim))
            elif hasattr(obj, 'to_matrix'):
                # If instruction is a gate first we see if it has a
                # `to_matrix` definition and if so use that.
                try:
                    kraus = [obj.to_matrix()]
                    dim = len(kraus[0])
                    chan = SuperOp(
                        _to_superop('Kraus', (kraus, None), dim, dim))
                except QiskitError:
                    pass
            if chan is not None:
                # Perform the composition and inplace update the current state
                # of the operator
                op = self.compose(chan, qargs=qargs)
                self._data = op.data
            else:
                # If the instruction doesn't have a matrix defined we use its
                # circuit decomposition definition if it exists, otherwise we
                # cannot compose this gate and raise an error.
                if obj.definition is None:
                    raise QiskitError('Cannot apply Instruction: {}'.format(
                        obj.name))
                for instr, qregs, cregs in obj.definition:
                    if cregs:
                        raise QiskitError(
                            'Cannot apply instruction with classical registers: {}'
                            .format(instr.name))
                    # Get the integer position of the flat register
                    new_qargs = [tup[1] for tup in qregs]
                    self._append_instruction(instr, qargs=new_qargs)
        else:
            raise QiskitError('Input is not an instruction.')

Response: Update the current Operator by apply an instruction.


Instruction:
Implement a function in Python 3 to
return a DAGCircuit with a barrier before last measurements.

Response:
def run(self, dag):
        """Return a circuit with a barrier before last measurements."""

        # Collect DAG nodes which are followed only by barriers or other measures.
        final_op_types = ['measure', 'barrier']
        final_ops = []
        for candidate_node in dag.named_nodes(*final_op_types):
            is_final_op = True

            for _, child_successors in dag.bfs_successors(candidate_node):

                if any(suc.type == 'op' and suc.name not in final_op_types
                       for suc in child_successors):
                    is_final_op = False
                    break

            if is_final_op:
                final_ops.append(candidate_node)

        if not final_ops:
            return dag

        # Create a layer with the barrier and add registers from the original dag.
        barrier_layer = DAGCircuit()
        for qreg in dag.qregs.values():
            barrier_layer.add_qreg(qreg)
        for creg in dag.cregs.values():
            barrier_layer.add_creg(creg)

        final_qubits = set(final_op.qargs[0] for final_op in final_ops)

        barrier_layer.apply_operation_back(
            Barrier(len(final_qubits)), list(final_qubits), [])

        # Preserve order of final ops collected earlier from the original DAG.
        ordered_final_nodes = [node for node in dag.topological_op_nodes()
                               if node in set(final_ops)]

        # Move final ops to the new layer and append the new layer to the DAG.
        for final_node in ordered_final_nodes:
            barrier_layer.apply_operation_back(final_node.op,
                                               final_node.qargs,
                                               final_node.cargs)

        for final_op in final_ops:
            dag.remove_op_node(final_op)

        dag.extend_back(barrier_layer)

        # Merge the new barrier into any other barriers
        adjacent_pass = MergeAdjacentBarriers()
        return adjacent_pass.run(dag)


Instruction:
Implement a function in Python 3 to
convert a list of QuantumCircuits into a Qobj.

Response:
def circuits_to_qobj(circuits, qobj_header=None,
                     qobj_id=None, backend_name=None,
                     config=None, shots=None, max_credits=None,
                     basis_gates=None,
                     coupling_map=None, seed=None, memory=None):
    """Convert a list of circuits into a qobj.

    Args:
        circuits (list[QuantumCircuits] or QuantumCircuit): circuits to compile
        qobj_header (QobjHeader): header to pass to the results
        qobj_id (int): TODO: delete after qiskit-terra 0.8
        backend_name (str): TODO: delete after qiskit-terra 0.8
        config (dict): TODO: delete after qiskit-terra 0.8
        shots (int): TODO: delete after qiskit-terra 0.8
        max_credits (int): TODO: delete after qiskit-terra 0.8
        basis_gates (str): TODO: delete after qiskit-terra 0.8
        coupling_map (list): TODO: delete after qiskit-terra 0.8
        seed (int): TODO: delete after qiskit-terra 0.8
        memory (bool): TODO: delete after qiskit-terra 0.8

    Returns:
        Qobj: the Qobj to be run on the backends
    """
    warnings.warn('circuits_to_qobj is deprecated and will be removed in Qiskit Terra 0.9. '
                  'Use qiskit.compiler.assemble() to serialize circuits into a qobj.',
                  DeprecationWarning)

    qobj_header = qobj_header or QobjHeader()

    if backend_name:
        qobj_header.backend_name = backend_name
    if basis_gates:
        warnings.warn('basis_gates was unused and will be removed.', DeprecationWarning)
    if coupling_map:
        warnings.warn('coupling_map was unused and will be removed.', DeprecationWarning)

    qobj = assemble(experiments=circuits,
                    qobj_id=qobj_id,
                    qobj_header=qobj_header,
                    shots=shots,
                    memory=memory,
                    max_credits=max_credits,
                    seed_simulator=seed,
                    config=config)

    return qobj


Instruction:
Create a Python 3 function to
expand 3 + qubit gates using their decomposition rules.

Response:
def run(self, dag):
        """Expand 3+ qubit gates using their decomposition rules.

        Args:
            dag(DAGCircuit): input dag
        Returns:
            DAGCircuit: output dag with maximum node degrees of 2
        Raises:
            QiskitError: if a 3q+ gate is not decomposable
        """
        for node in dag.threeQ_or_more_gates():
            # TODO: allow choosing other possible decompositions
            rule = node.op.definition
            if not rule:
                raise QiskitError("Cannot unroll all 3q or more gates. "
                                  "No rule to expand instruction %s." %
                                  node.op.name)

            # hacky way to build a dag on the same register as the rule is defined
            # TODO: need anonymous rules to address wires by index
            decomposition = DAGCircuit()
            decomposition.add_qreg(rule[0][1][0][0])
            for inst in rule:
                decomposition.apply_operation_back(*inst)
            decomposition = self.run(decomposition)  # recursively unroll
            dag.substitute_node_with_dag(node, decomposition)
        return dag


Instruction:
Make a summary of the following Python 3 code
def run(self, dag):
        # Walk through the DAG and expand each non-basis node
        for node in dag.op_nodes(self.gate):
            # opaque or built-in gates are not decomposable
            if not node.op.definition:
                continue
            # TODO: allow choosing among multiple decomposition rules
            rule = node.op.definition
            # hacky way to build a dag on the same register as the rule is defined
            # TODO: need anonymous rules to address wires by index
            decomposition = DAGCircuit()
            decomposition.add_qreg(rule[0][1][0][0])
            if rule[0][2]:
                decomposition.add_creg(rule[0][2][0][0])
            for inst in rule:
                decomposition.apply_operation_back(*inst)
            dag.substitute_node_with_dag(node, decomposition)
        return dag

Response: Expand a given gate into its decomposition.


Instruction:
Can you implement a function in Python 3 that
applies u2 to q.

Response:
def unitary(self, obj, qubits, label=None):
    """Apply u2 to q."""
    if isinstance(qubits, QuantumRegister):
        qubits = qubits[:]
    return self.append(UnitaryGate(obj, label=label), qubits, [])


Instruction:
Write a Python 3 script to
calculate a subcircuit that implements this unitary.

Response:
def _define(self):
        """Calculate a subcircuit that implements this unitary."""
        if self.num_qubits == 1:
            q = QuantumRegister(1, "q")
            angles = euler_angles_1q(self.to_matrix())
            self.definition = [(U3Gate(*angles), [q[0]], [])]
        if self.num_qubits == 2:
            self.definition = two_qubit_kak(self.to_matrix())


Instruction:
Can you generate a brief explanation for the following Python 3 code
def check_type(self, value, attr, data):
        if self.many and not is_collection(value):
            raise self._not_expected_type(
                value, Iterable, fields=[self], field_names=attr, data=data)

        _check_type = super().check_type

        errors = []
        values = value if self.many else [value]
        for idx, v in enumerate(values):
            try:
                _check_type(v, idx, values)
            except ValidationError as err:
                errors.append(err.messages)

        if errors:
            errors = errors if self.many else errors[0]
            raise ValidationError(errors)

        return value

Response: Validate if the value is of the type of the schema s model.


Instruction:
Can you generate the documentation for the following Python 3 function
def check_type(self, value, attr, data):
        super().check_type(value, attr, data)

        errors = []
        for idx, v in enumerate(value):
            try:
                self.container.check_type(v, idx, value)
            except ValidationError as err:
                errors.append(err.messages)

        if errors:
            raise ValidationError(errors)

        return value

Response: Validate if the value is a list of valid item - field values.


Instruction:
Can you create a Python 3 function that
draws the graph of the dag.

Response:
def dag_drawer(dag, scale=0.7, filename=None, style='color'):
    """Plot the directed acyclic graph (dag) to represent operation dependencies
    in a quantum circuit.

    Note this function leverages
    `pydot <https://github.com/erocarrera/pydot>`_ (via
    `nxpd <https://github.com/chebee7i/nxpd`_) to generate the graph, which
    means that having `Graphviz <https://www.graphviz.org/>`_ installed on your
    system is required for this to work.

    Args:
        dag (DAGCircuit): The dag to draw.
        scale (float): scaling factor
        filename (str): file path to save image to (format inferred from name)
        style (str): 'plain': B&W graph
                     'color' (default): color input/output/op nodes

    Returns:
        Ipython.display.Image: if in Jupyter notebook and not saving to file,
            otherwise None.

    Raises:
        VisualizationError: when style is not recognized.
        ImportError: when nxpd or pydot not installed.
    """
    try:
        import nxpd
        import pydot  # pylint: disable=unused-import
    except ImportError:
        raise ImportError("dag_drawer requires nxpd, pydot, and Graphviz. "
                          "Run 'pip install nxpd pydot', and install graphviz")

    G = dag.to_networkx()
    G.graph['dpi'] = 100 * scale

    if style == 'plain':
        pass
    elif style == 'color':
        for node in G.nodes:
            n = G.nodes[node]
            n['label'] = node.name
            if node.type == 'op':
                n['color'] = 'blue'
                n['style'] = 'filled'
                n['fillcolor'] = 'lightblue'
            if node.type == 'in':
                n['color'] = 'black'
                n['style'] = 'filled'
                n['fillcolor'] = 'green'
            if node.type == 'out':
                n['color'] = 'black'
                n['style'] = 'filled'
                n['fillcolor'] = 'red'
        for e in G.edges(data=True):
            e[2]['label'] = e[2]['name']
    else:
        raise VisualizationError("Unrecognized style for the dag_drawer.")

    if filename:
        show = False
    elif ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
        show = 'ipynb'
    else:
        show = True

    return nxpd.draw(G, filename=filename, show=show)


Instruction:
Here you have a function in Python 3, explain what it does
def _atol(self, atol):
        # NOTE: that this overrides the class value so applies to all
        # instances of the class.
        max_tol = self.__class__.MAX_TOL
        if atol < 0:
            raise QiskitError("Invalid atol: must be non-negative.")
        if atol > max_tol:
            raise QiskitError(
                "Invalid atol: must be less than {}.".format(max_tol))
        self.__class__.ATOL = atol

Response: Set the absolute tolerence parameter for float comparisons.


Instruction:
Write a Python 3 function for
setting the relative tolerence parameter for float comparisons.

Response:
def _rtol(self, rtol):
        """Set the relative tolerence parameter for float comparisons."""
        # NOTE: that this overrides the class value so applies to all
        # instances of the class.
        max_tol = self.__class__.MAX_TOL
        if rtol < 0:
            raise QiskitError("Invalid rtol: must be non-negative.")
        if rtol > max_tol:
            raise QiskitError(
                "Invalid rtol: must be less than {}.".format(max_tol))
        self.__class__.RTOL = rtol


Instruction:
Implement a function in Python 3 to
return tuple of input dimension for specified subsystems.

Response:
def input_dims(self, qargs=None):
        """Return tuple of input dimension for specified subsystems."""
        if qargs is None:
            return self._input_dims
        return tuple(self._input_dims[i] for i in qargs)


Instruction:
Can you tell what is the following Python 3 function doing
def output_dims(self, qargs=None):
        if qargs is None:
            return self._output_dims
        return tuple(self._output_dims[i] for i in qargs)

Response: Return tuple of output dimension for specified subsystems.