Spelling fixes (#2392)

* Spelling fixes

* More spelling fixes

* Fix line-length lint fail due to typo fix

* A few more typos

qiskit/assembler/assemble_circuits.py

@@ -26,7 +26,7 @@ def assemble_circuits(circuits, run_config, qobj_id, qobj_header):
     """Assembles a list of circuits into a qobj which can be run on the backend.
 
     Args:
-        circuits (list[QuantumCircuits]): circuit(s) to assemble
+        circuits (list[QuantumCircuit]): circuit(s) to assemble
         qobj_id (int): identifier for the generated qobj
         qobj_header (QobjHeader): header to pass to the results
         run_config (RunConfig): configuration of the runtime environment

qiskit/assembler/assemble_schedules.py

@@ -83,7 +83,7 @@ def assemble_schedules(schedules, qobj_id, qobj_header, run_config):
     qobj_config['pulse_library'] = [PulseLibraryItem(name=pulse.name, samples=pulse.samples)
                                     for pulse in user_pulselib]
 
-    # create qob experiment field
+    # create qobj experiment field
     experiments = []
     schedule_los = qobj_config.pop('schedule_los', [])
 

qiskit/assembler/disassemble.py

@@ -92,10 +92,10 @@ def _experiments_to_circuits(qobj):
 
 
 def disassemble(qobj):
-    """Dissasemble a qobj and return the circuits, run_config, and user header
+    """Disassemble a qobj and return the circuits, run_config, and user header
 
     Args:
-        qobj (Qobj): The input qobj object to dissasemble
+        qobj (Qobj): The input qobj object to disassemble
     Returns:
         circuits (list): A list of quantum circuits
         run_config (dict): The dist of the run config

qiskit/circuit/instruction.py

@@ -28,7 +28,7 @@
 
     num_qubits, num_clbits: dimensions of the instruction
 
-    params: List of parameters to specialize a specific intruction instance.
+    params: List of parameters to specialize a specific instruction instance.
 
 Instructions do not have any context about where they are in a circuit (which qubits/clbits).
 The circuit itself keeps this context.
@@ -55,7 +55,7 @@ def __init__(self, name, num_qubits, num_clbits, params):
         Args:
             name (str): instruction name
             num_qubits (int): instruction's qubit width
-            num_clbits (int): instructions's clbit width
+            num_clbits (int): instruction's clbit width
             params (list[sympy.Basic|qasm.Node|int|float|complex|str|ndarray]): list of parameters
         Raises:
             QiskitError: when the register is not in the correct format.

qiskit/circuit/parametertable.py

@@ -12,7 +12,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 """
-Look-up table for varaible parameters in QuantumCircuit.
+Look-up table for variable parameters in QuantumCircuit.
 """
 from collections.abc import MutableMapping
 

qiskit/dagcircuit/dagcircuit.py

@@ -529,7 +529,7 @@ def compose_back(self, input_circuit, edge_map=None):
                 of self.
 
         Raises:
-            DAGCircuitError: if missing, duplicate or incosistent wire
+            DAGCircuitError: if missing, duplicate or inconsistent wire
         """
         edge_map = edge_map or {}
 
@@ -815,7 +815,7 @@ def substitute_node_with_dag(self, node, input_dag, wires=None):
             node = self._id_to_node[node]
 
         condition = node.condition
-        # the dag must be ammended if used in a
+        # the dag must be amended if used in a
         # conditional context. delete the op nodes and replay
         # them with the condition.
         if condition:

qiskit/extensions/unitary.py

@@ -53,7 +53,7 @@ def __init__(self, data, label=None):
             # the object to an Operator so that we can extract the underlying
             # numpy matrix from `Operator.data`.
             data = data.to_operator().data
-        # Convert to numpy array incase not already an array
+        # Convert to numpy array in case not already an array
         data = numpy.array(data, dtype=complex)
         # Check input is unitary
         if not is_unitary_matrix(data):

qiskit/providers/basicaer/basicaertools.py

@@ -160,7 +160,7 @@ def _einsum_matmul_index_helper(gate_indices, number_of_qubits):
     if len(gate_indices) + number_of_qubits > 26:
         raise QiskitError("Total number of free indexes limited to 26")
 
-    # Indicies for N-qubit input tensor
+    # Indices for N-qubit input tensor
     tens_in = ascii_lowercase[:number_of_qubits]
 
     # Indices for the N-qubit output tensor

qiskit/providers/basicaer/statevector_simulator.py

@@ -119,7 +119,7 @@ def run(self, qobj, backend_options=None):
             zero state. This size of this vector must be correct for the number
             of qubits in all experiments in the qobj.
 
-            The "chop_threshold" option specifies a trunctation value for
+            The "chop_threshold" option specifies a truncation value for
             setting small values to zero in the output statevector. The default
             value is 1e-15.
 

qiskit/providers/basicaer/unitary_simulator.py

@@ -240,7 +240,7 @@ def run(self, qobj, backend_options=None):
             matrix. This size of this matrix must be correct for the number
             of qubits inall experiments in the qobj.
 
-            The "chop_threshold" option specifies a trunctation value for
+            The "chop_threshold" option specifies a truncation value for
             setting small values to zero in the output unitary. The default
             value is 1e-15.
 

qiskit/providers/models/backendconfiguration.py

@@ -272,7 +272,7 @@ class PulseBackendConfiguration(BackendConfiguration):
             with form (min, max) in GHz.
         dt (float): Qubit drive channel timestep in nanoseconds.
         dtm (float): Measurement drive channel timestep in nanoseconds.
-        rep_times (list[float]): Supported repition times for device in microseconds.
+        rep_times (list[float]): Supported repetition times for device in microseconds.
         meas_kernels (list[str]): Supported measurement kernels.
         discriminators: Supported discriminators.
         **kwargs: Optional fields.

qiskit/pulse/pulse_lib/continuous.py

@@ -101,7 +101,7 @@ def _fix_gaussian_width(gaussian_samples, amp: float, center: float, sigma: floa
                         ret_scale_factor: bool = False) -> np.ndarray:
     r"""Enforce that the supplied gaussian pulse is zeroed at a specific width.
 
-    This is acheived by subtracting $\Omega_g(center \pm zeroed_width/2)$ from all samples.
+    This is achieved by subtracting $\Omega_g(center \pm zeroed_width/2)$ from all samples.
 
     amp: Pulse amplitude at `2\times center+1`.
     center: Center (mean) of pulse.
@@ -229,7 +229,7 @@ def drag(times: np.ndarray, amp: complex, center: float, sigma: float, beta: flo
         sigma: Width (standard deviation) of pulse.
         beta: Y correction amplitude. For the SNO this is $\beta=-\frac{\lambda_1^2}{4\Delta_2}$.
             Where $\lambds_1$ is the relative coupling strength between the first excited and second
-            excited states and $\Delta_2$ is the detuning between the resepective excited states.
+            excited states and $\Delta_2$ is the detuning between the respective excited states.
         zeroed_width: Subtract baseline to gaussian pulses to make sure
                  $\Omega_g(center \pm zeroed_width/2)=0$ is satisfied. This is used to avoid
                  large discontinuities at the start of a gaussian pulse.

qiskit/pulse/pulse_lib/discrete.py

@@ -242,7 +242,7 @@ def drag(duration: int, amp: complex, sigma: float, beta: float, name: str = Non
         sigma: Width (standard deviation) of pulse.
         beta: Y correction amplitude. For the SNO this is $\beta=-\frac{\lambda_1^2}{4\Delta_2}$.
             Where $\lambds_1$ is the relative coupling strength between the first excited and second
-            excited states and $\Delta_2$ is the detuning between the resepective excited states.
+            excited states and $\Delta_2$ is the detuning between the respective excited states.
         name: Name of pulse.
     """
     center = duration/2

qiskit/pulse/samplers/decorators.py

@@ -112,7 +112,7 @@ def linear(times: np.ndarray, m: float, b: float) -> np.ndarray:
 This in itself is not sufficient as the signature of the sampled function has
 `duration`, whereas the signature of the continuous function is `time`.
 
-This is acheived by removing `__wrapped__` set by `functools.wraps` in order to preserve
+This is achieved by removing `__wrapped__` set by `functools.wraps` in order to preserve
 the correct signature and also applying `_update_annotations` and `_update_docstring`
 to the generated function which corrects the function annotations and adds an informative
 docstring respectively.
@@ -209,7 +209,7 @@ def generate_sampler(continuous_pulse: Callable) -> Callable:
         @functools.wraps(continuous_pulse)
         def call_sampler(duration: int, *args, **kwargs) -> commands.SamplePulse:
             """Replace the call to the continuous function with a call to the sampler applied
-            to the anlytic pulse function."""
+            to the analytic pulse function."""
             sampled_pulse = sample_function(continuous_pulse, duration, *args, **kwargs)
             return np.asarray(sampled_pulse, dtype=np.complex_)
 

qiskit/pulse/samplers/strategies.py

@@ -20,7 +20,7 @@ def f(times: np.ndarray, *args, **kwargs) -> np.ndarray:
         ...
     ```
 The sampler will call the `continuous_pulse` function with a set of times it will decide
-according to the sampling strategy it implments along with the passed `args` and `kwargs`.
+according to the sampling strategy it implements along with the passed `args` and `kwargs`.
 """
 
 from typing import Callable

qiskit/qasm/qasmparser.py

@@ -787,7 +787,7 @@ def p_gate_op_5e(self, program):
     #        | OPAQUE id gate_scope '(' ')'              bit_list
     #        | OPAQUE id gate_scope '(' gate_id_list ')' bit_list
     #
-    # These are like gate declarations only wihtout a body.
+    # These are like gate declarations only without a body.
     # ----------------------------------------
     def p_opaque_0(self, program):
         """

qiskit/quantum_info/operators/base_operator.py

@@ -68,12 +68,12 @@ def data(self):
 
     @property
     def _atol(self):
-        """The absolute tolerence parameter for float comparisons."""
+        """The absolute tolerance parameter for float comparisons."""
         return self.__class__.ATOL
 
     @_atol.setter
     def _atol(self, atol):
-        """Set the absolute tolerence parameter for float comparisons."""
+        """Set the absolute tolerance 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
@@ -86,12 +86,12 @@ def _atol(self, atol):
 
     @property
     def _rtol(self):
-        """The relative tolerence parameter for float comparisons."""
+        """The relative tolerance parameter for float comparisons."""
         return self.__class__.RTOL
 
     @_rtol.setter
     def _rtol(self, rtol):
-        """Set the relative tolerence parameter for float comparisons."""
+        """Set the relative tolerance 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
@@ -333,7 +333,7 @@ def _einsum_matmul(cls, tensor, mat, indices, shift=0, right_mul=False):
             tensor (np.array): a vector or matrix reshaped to a rank-N tensor.
             mat (np.array): a matrix reshaped to a rank-2M tensor.
             indices (list): tensor indices to contract with mat.
-            shift (int): shift for indicies of tensor to contract [Default: 0].
+            shift (int): shift for indices of tensor to contract [Default: 0].
             right_mul (bool): if True right multiply tensor by mat
                               (else left multiply) [Default: False].
 
@@ -352,7 +352,7 @@ def _einsum_matmul(cls, tensor, mat, indices, shift=0, right_mul=False):
         indices_tensor = list(range(rank))
         for j, index in enumerate(indices):
             indices_tensor[index + shift] = rank + j
-        # Get einsum indces for mat
+        # Get einsum indices for mat
         mat_contract = list(reversed(range(rank, rank + len(indices))))
         mat_free = [index + shift for index in reversed(indices)]
         if right_mul:

qiskit/quantum_info/operators/channel/chi.py

@@ -94,7 +94,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
                 # convert it to a SuperOp
                 data = SuperOp._instruction_to_superop(data)
             else:
-                # We use the QuantumChannel init transform to intialize
+                # We use the QuantumChannel init transform to initialize
                 # other objects into a QuantumChannel or Operator object.
                 data = self._init_transformer(data)
             input_dim, output_dim = data.dim

qiskit/quantum_info/operators/channel/choi.py

@@ -61,7 +61,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
         automatically determined from the input data. If the input data is
         a Numpy array of shape (4**N, 4**N) qubit systems will be used. If
         the input operator is not an N-qubit operator, it will assign a
-        single subsystem with dimension specifed by the shape of the input.
+        single subsystem with dimension specified by the shape of the input.
         """
         # If the input is a raw list or matrix we assume that it is
         # already a Choi matrix.
@@ -94,7 +94,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
                 # convert it to a SuperOp
                 data = SuperOp._instruction_to_superop(data)
             else:
-                # We use the QuantumChannel init transform to intialize
+                # We use the QuantumChannel init transform to initialize
                 # other objects into a QuantumChannel or Operator object.
                 data = self._init_transformer(data)
             input_dim, output_dim = data.dim
@@ -124,7 +124,7 @@ def transpose(self):
         # 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
+        # Swap input and output indices 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))

qiskit/quantum_info/operators/channel/kraus.py

@@ -10,7 +10,7 @@
 Kraus representation of a Quantum Channel.
 
 
-The Kraus representation for a quantum channel E is given by a set of matrics [A_i] such that
+The Kraus representation for a quantum channel E is given by a set of matrices [A_i] such that
 
     E(ρ) = sum_i A_i.ρ.A_i^dagger
 
@@ -66,7 +66,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
         automatically determined from the input data. If the input data is
         a list of Numpy arrays of shape (2**N, 2**N) qubit systems will be used. If
         the input does not correspond to an N-qubit channel, it will assign a
-        single subsystem with dimension specifed by the shape of the input.
+        single subsystem with dimension specified by the shape of the input.
         """
         # If the input is a list or tuple we assume it is a list of Kraus
         # matrices, if it is a numpy array we assume that it is a single Kraus
@@ -128,7 +128,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
                 # convert it to a SuperOp
                 data = SuperOp._instruction_to_superop(data)
             else:
-                # We use the QuantumChannel init transform to intialize
+                # We use the QuantumChannel init transform to initialize
                 # other objects into a QuantumChannel or Operator object.
                 data = self._init_transformer(data)
             input_dim, output_dim = data.dim

qiskit/quantum_info/operators/channel/ptm.py

@@ -89,7 +89,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
                 # convert it to a SuperOp
                 data = SuperOp._instruction_to_superop(data)
             else:
-                # We use the QuantumChannel init transform to intialize
+                # We use the QuantumChannel init transform to initialize
                 # other objects into a QuantumChannel or Operator object.
                 data = self._init_transformer(data)
             input_dim, output_dim = data.dim

qiskit/quantum_info/operators/channel/quantum_channel.py

@@ -119,7 +119,7 @@ def _format_state(self, state, density_matrix=False):
             if shape[1] != 1 and shape[1] != shape[0]:
                 raise QiskitError('Input state is not a vector or matrix.')
             if shape[1] == 1:
-                # flatten colum-vector to vector
+                # flatten column-vector to vector
                 state = np.reshape(state, shape[0])
         # Convert statevector to density matrix if required
         if density_matrix and ndim == 1:

qiskit/quantum_info/operators/channel/stinespring.py

@@ -68,7 +68,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
         """
         # If the input is a list or tuple we assume it is a pair of general
         # Stinespring matrices. If it is a numpy array we assume that it is
-        # a single stinespring matrix.
+        # a single Stinespring matrix.
         if isinstance(data, (list, tuple, np.ndarray)):
             if not isinstance(data, tuple):
                 # Convert single Stinespring set to length 1 tuple
@@ -81,7 +81,7 @@ def __init__(self, data, input_dims=None, output_dims=None):
                              np.array(data[1], dtype=complex))
 
             dim_left, dim_right = stine[0].shape
-            # If two stinespring matrices check they are same shape
+            # If two Stinespring matrices check they are same shape
             if stine[1] is not None:
                 if stine[1].shape != (dim_left, dim_right):
                     raise QiskitError("Invalid Stinespring input.")
@@ -354,7 +354,7 @@ def _evolve(self, state, qargs=None):
             )
         if state.ndim == 1 and self._data[1] is None and \
            self._data[0].shape[0] // self._output_dim == 1:
-            # If the shape of the stinespring operator is equal to the output_dim
+            # If the shape of the Stinespring operator is equal to the output_dim
             # evolution of a state vector psi -> stine.psi
             return np.dot(self._data[0], state)
         # Otherwise we always return a density matrix
@@ -385,7 +385,7 @@ def _tensor_product(self, other, reverse=False):
         if not isinstance(other, Stinespring):
             other = Stinespring(other)
 
-        # Tensor stinespring ops
+        # Tensor Stinespring ops
         sa_l, sa_r = self._data
         sb_l, sb_r = other._data
 
@@ -401,7 +401,7 @@ def _tensor_product(self, other, reverse=False):
             shape_in = (dout_a, dtr_a, dout_b, dtr_b, din_a * din_b)
             shape_out = (dout_a * dtr_a * dout_b * dtr_b, din_a * din_b)
 
-        # Compute left stinepsring op
+        # Compute left Stinespring op
         if reverse:
             input_dims = self.input_dims() + other.input_dims()
             output_dims = self.output_dims() + other.output_dims()
@@ -410,12 +410,12 @@ def _tensor_product(self, other, reverse=False):
             input_dims = other.input_dims() + self.input_dims()
             output_dims = other.output_dims() + self.output_dims()
             sab_l = np.kron(sa_l, sb_l)
-        # Reravel indicies
+        # Reravel indices
         sab_l = np.reshape(
             np.transpose(np.reshape(sab_l, shape_in), (0, 2, 1, 3, 4)),
             shape_out)
 
-        # Compute right stinespring op
+        # Compute right Stinespring op
         if sa_r is None and sb_r is None:
             sab_r = None
         else:
@@ -427,7 +427,7 @@ def _tensor_product(self, other, reverse=False):
                 sab_r = np.kron(sb_r, sa_r)
             else:
                 sab_r = np.kron(sa_r, sb_r)
-            # Reravel indicies
+            # Reravel indices
             sab_r = np.reshape(
                 np.transpose(np.reshape(sab_r, shape_in), (0, 2, 1, 3, 4)),
                 shape_out)