Fix miscellaneous trivial typos

.github/workflows/ci-nightly-cirq-test.yaml

@@ -19,7 +19,7 @@
 # For testing, this workflow can be invoked manually from the GitHub page at
 # https://github.com/tensorflow/quantum/actions/workflows/ci-nightly-cirq-test.yaml
 # Clicking the "Run workflow" button there will present a form interface with
-# options for overridding some of the parameters for the run.
+# options for overriding some of the parameters for the run.
 
 # yamllint disable rule:line-length
 

.github/workflows/ci.yaml

@@ -118,7 +118,7 @@ jobs:
   # 2024-11-30 [mhucka] temporarily turning off leak-tests because it produces
   # false positives on GH that we can't immediately address. TODO: if updating
   # TFQ to use Clang and the latest TF does not resolve this, find a way to
-  # skip the handful of failing tests and renable the rest of the msan tests.
+  # skip the handful of failing tests and re-enable the rest of the msan tests.
   #
   # leak-tests:
   #   name: Memory Leak tests

configure.sh

@@ -201,7 +201,7 @@ fi
 
 write_bazelrc ""
 
-# The following supressions are for warnings coming from external dependencies.
+# The following suppressions are for warnings coming from external dependencies.
 # They're most likely inconsequential or false positives. Since we can't fix
 # them, we suppress the warnings to reduce noise. Note: single quotes are needed
 # for the first two so that the $ anchors are preserved in the .bazelrc file.

release/README.md

@@ -82,7 +82,7 @@ into the upstream repository before proceeding further.
 
     This will update the dependency versions in the file `requirements.txt` to
     the latest versions based on `requirements.in`. If this process fails, you
-    may have to iterate on adjusting the contraints in `requirements.in`
+    may have to iterate on adjusting the constraints in `requirements.in`
     followed by running `generate_requirements.sh` again until it succeeds.
 
 3.  If any changes were made to `requirements.in`, check the requirements listed

tensorflow_quantum/core/ops/batch_util.py

@@ -73,7 +73,7 @@ def on_job_result(self, job, result):
         self._ones[job_id] += parities[1]
 
     def collect(self, sampler):
-        """Synchronus collect."""
+        """Synchronous collect."""
         # See #562, this is a workaround to an event loop issue in the tutorials
         # see also:
         # https://stackoverflow.com/questions/55409641/asyncio-run-cannot-be-called-from-a-running-event-loop

tensorflow_quantum/core/ops/cirq_ops.py

@@ -437,7 +437,7 @@ def cirq_sample(programs, symbol_names, symbol_values, num_samples):
         symbol labeled 'b' and 1 into the symbol labeled 'c'. Then it would
         draw 100 samples from the circuit.
 
-        Note: In the case of circuits with varying size, all nonexistant
+        Note: In the case of circuits with varying size, all nonexistent
         samples for a particular circuit are padded with -2.
 
         Args:

tensorflow_quantum/core/ops/cirq_ops_test.py

@@ -295,7 +295,7 @@ def test_simulate_state_output_padding(self, op_and_sim, all_n_qubits):
         for circuit in circuit_batch:
             result = sim.simulate(circuit)
 
-            # density matricies should be zero everywhere except for the
+            # density matrices should be zero everywhere except for the
             # top left corner
             if isinstance(result, cirq.DensityMatrixTrialResult):
                 dm = result.final_density_matrix

tensorflow_quantum/core/ops/math_ops/fidelity_op.py

@@ -70,7 +70,7 @@ def fidelity(programs, symbol_names, symbol_values, other_programs):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         other_programs: `tf.Tensor` of strings with shape [batch_size, n_others]
             containing the string representations of the circuits with which to

tensorflow_quantum/core/ops/math_ops/inner_product_grad_test.py

@@ -122,7 +122,7 @@ def test_inner_product_grad_inputs(self):
         with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                     'not found in reference circuit'):
             # other_programs tensor has the right type but operates on
-            # qubits that the reference ciruit doesn't have.
+            # qubits that the reference circuit doesn't have.
             new_qubits = [cirq.GridQubit(5, 5), cirq.GridQubit(9, 9)]
             new_circuits, _ = util.random_circuit_resolver_batch(
                 new_qubits, batch_size)
@@ -134,7 +134,7 @@ def test_inner_product_grad_inputs(self):
         with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                     'not found in paired circuit'):
             # other_programs tensor has the right type but operates on
-            # qubits that the reference ciruit doesn't have.
+            # qubits that the reference circuit doesn't have.
             new_qubits = cirq.GridQubit.rect(1, n_qubits - 1)
             new_circuits, _ = util.random_circuit_resolver_batch(
                 new_qubits, batch_size)

tensorflow_quantum/core/ops/math_ops/inner_product_op.py

@@ -46,7 +46,7 @@ def _inner_product_grad(programs, symbol_names, symbol_values, other_programs,
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         other_programs: `tf.Tensor` of strings with shape [batch_size, n_others]
             containing the string representations of the circuits with which to
@@ -122,7 +122,7 @@ def inner_product(programs, symbol_names, symbol_values, other_programs):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         other_programs: `tf.Tensor` of strings with shape [batch_size, n_others]
             containing the string representations of the circuits with which to

tensorflow_quantum/core/ops/math_ops/inner_product_op_test.py

@@ -115,7 +115,7 @@ def test_inner_product_inputs(self):
         with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                     'not found in reference circuit'):
             # other_programs tensor has the right type but operates on
-            # qubits that the reference ciruit doesn't have.
+            # qubits that the reference circuit doesn't have.
             new_qubits = [cirq.GridQubit(5, 5), cirq.GridQubit(9, 9)]
             new_circuits, _ = util.random_circuit_resolver_batch(
                 new_qubits, batch_size)
@@ -127,7 +127,7 @@ def test_inner_product_inputs(self):
         with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
                                     'not found in paired circuit'):
             # other_programs tensor has the right type but operates on
-            # qubits that the reference ciruit doesn't have.
+            # qubits that the reference circuit doesn't have.
             new_qubits = cirq.GridQubit.rect(1, n_qubits - 1)
             new_circuits, _ = util.random_circuit_resolver_batch(
                 new_qubits, batch_size)

tensorflow_quantum/core/ops/math_ops/simulate_mps.py

@@ -42,7 +42,7 @@ def mps_1d_expectation(programs,
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will
@@ -127,7 +127,7 @@ def mps_1d_sampled_expectation(programs,
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will

tensorflow_quantum/core/ops/noise/noisy_expectation_op.py

@@ -75,7 +75,7 @@ def expectation(programs, symbol_names, symbol_values, pauli_sums, num_samples):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will

tensorflow_quantum/core/ops/noise/noisy_sampled_expectation_op.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Module for high performance noisy circuit sampled epxectation ops."""
+"""Module for high performance noisy circuit sampled expectation ops."""
 import os
 import tensorflow as tf
 from tensorflow_quantum.core.ops.load_module import load_module
@@ -74,7 +74,7 @@ def sampled_expectation(programs, symbol_names, symbol_values, pauli_sums,
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will

tensorflow_quantum/core/ops/noise/tfq_noisy_samples.cc

@@ -159,7 +159,7 @@ class TfqNoisySamplesOp : public tensorflow::OpKernel {
 
     // Simulate programs one by one. Parallelizing over state vectors
     // we no longer parallelize over circuits. Each time we encounter a
-    // a larger circuit we will grow the Statevector as nescessary.
+    // a larger circuit we will grow the Statevector as necessary.
     for (size_t i = 0; i < ncircuits.size(); i++) {
       int nq = num_qubits[i];
 

tensorflow_quantum/core/ops/parse_context.cc

@@ -52,7 +52,7 @@ Status ParseProto(const std::string& text, T* proto) {
 
   return Status(
       static_cast<tensorflow::errors::Code>(absl::StatusCode::kInvalidArgument),
-      "Unparseable proto: " + text);
+      "Unparsable proto: " + text);
 }
 
 }  // namespace

tensorflow_quantum/core/ops/tfq_adj_grad_op.py

@@ -31,7 +31,7 @@ def tfq_adj_grad(programs, symbol_names, symbol_values, pauli_sums, prev_grad):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will

tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc

@@ -115,7 +115,7 @@ class TfqCalculateUnitaryOp : public tensorflow::OpKernel {
 
     // Simulate programs one by one. Parallelizing over state vectors
     // we no longer parallelize over circuits. Each time we encounter a
-    // a larger circuit we will grow the unitary as nescessary.
+    // a larger circuit we will grow the unitary as necessary.
     for (size_t i = 0; i < fused_circuits.size(); i++) {
       int nq = num_qubits[i];
       UCalculator sim = UCalculator(tfq_for);

tensorflow_quantum/core/ops/tfq_ps_util_ops_test.py

@@ -304,7 +304,7 @@ def test_moment_preservation(self):
         # The non-decomposed moments should be the same.
         self.assertEqual(decomposed_programs[0][0], circuit_batch[0][0])
         self.assertEqual(decomposed_programs[0][-1], circuit_batch[0][-1])
-        # Check paralellized decompose gates in Moment[1]~[3].
+        # Check parallelized decompose gates in Moment[1]~[3].
         # The target ops are replaced by the first decomposition gates. It means
         # the first Moment has exactly the same number of gate ops.
         self.assertEqual(len(decomposed_programs[0][1]),
@@ -343,7 +343,7 @@ def test_more_complex_moment_preservation(self):
         # The non-decomposed moments should be the same.
         self.assertEqual(decomposed_programs[0][0], circuit_batch[0][0])
         self.assertEqual(decomposed_programs[0][-1], circuit_batch[0][-1])
-        # Check paralellized decompose gates in Moment[1]~[3].
+        # Check parallelized decompose gates in Moment[1]~[3].
         # The target ops are replaced by the first decomposition gates. It means
         # the first Moment has exactly the same number of gate ops.
         self.assertEqual(len(decomposed_programs[0][1]),

tensorflow_quantum/core/ops/tfq_simulate_ops.py

@@ -31,7 +31,7 @@ def tfq_simulate_expectation(programs, symbol_names, symbol_values, pauli_sums):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will
@@ -60,7 +60,7 @@ def tfq_simulate_state(programs, symbol_names, symbol_values):
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
     Returns:
         A `tf.Tensor` containing the final state of each circuit in `programs`.
@@ -116,7 +116,7 @@ def tfq_simulate_sampled_expectation(programs, symbol_names, symbol_values,
             `programs`.
         symbol_values: `tf.Tensor` of real numbers with shape
             [batch_size, n_params] specifying parameter values to resolve
-            into the circuits specificed by programs, following the ordering
+            into the circuits specified by programs, following the ordering
             dictated by `symbol_names`.
         pauli_sums: `tf.Tensor` of strings with shape [batch_size, n_ops]
             containing the string representation of the operators that will

tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc

@@ -153,7 +153,7 @@ class TfqSimulateSamplesOp : public tensorflow::OpKernel {
 
     // Simulate programs one by one. Parallelizing over state vectors
     // we no longer parallelize over circuits. Each time we encounter a
-    // a larger circuit we will grow the Statevector as nescessary.
+    // a larger circuit we will grow the Statevector as necessary.
     for (size_t i = 0; i < fused_circuits.size(); i++) {
       int nq = num_qubits[i];
 

tensorflow_quantum/core/ops/tfq_simulate_state_op.cc

@@ -135,7 +135,7 @@ class TfqSimulateStateOp : public tensorflow::OpKernel {
 
     // Simulate programs one by one. Parallelizing over state vectors
     // we no longer parallelize over circuits. Each time we encounter a
-    // a larger circuit we will grow the Statevector as nescessary.
+    // a larger circuit we will grow the Statevector as necessary.
     for (size_t i = 0; i < fused_circuits.size(); i++) {
       int nq = num_qubits[i];
 

tensorflow_quantum/core/ops/tfq_utility_ops_test.py

@@ -93,7 +93,7 @@ def test_append_input_checking(self):
     def test_append_circuit(self, max_n_bits, symbols, n_circuits):
         """Generate a bunch of circuits of different lengths acting on different
         numbers of qubits and append them using our op, checking that results
-        are consistant with the native cirq method.
+        are consistent with the native cirq method.
         """
         base_circuits = []
         circuits_to_append = []

tensorflow_quantum/core/serialize/op_serializer.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""op_serializer.py adapated from Cirq release 0.9.0"""
+"""op_serializer.py adapted from Cirq release 0.9.0"""
 
 from typing import List
 import cirq

tensorflow_quantum/core/serialize/op_serializer_test.py

@@ -327,7 +327,7 @@ def test_to_proto_no_getattr(self, q):
 
     @parameterized.parameters([cirq.GridQubit(1, 2), cirq.LineQubit(4)])
     def test_to_proto_not_required_ok(self, q):
-        """Test non require arg absense succeeds."""
+        """Test non require arg absence succeeds."""
         serializer = op_serializer.GateOpSerializer(
             gate_type=GateWithProperty,
             serialized_gate_id='my_gate',

tensorflow_quantum/core/src/circuit_parser_qsim.cc

@@ -102,7 +102,7 @@ inline Status ParseProtoControls(const Operation& op,
 
   if (control_toks.size() != control_v_toks.size()) {
     return Status(absl::StatusCode::kInvalidArgument,
-                  "Mistmatched number of control qubits and control values.");
+                  "Mismatched number of control qubits and control values.");
   }
   if (control_toks.empty()) {
     return ::tensorflow::Status();
@@ -121,7 +121,7 @@ inline Status ParseProtoControls(const Operation& op,
     valid = absl::SimpleAtoi(tok, &tmp);
     if (!valid) {
       return Status(absl::StatusCode::kInvalidArgument,
-                    "Unparseable control value: " + std::string(tok));
+                    "Unparsable control value: " + std::string(tok));
     }
     control_values->push_back(tmp);
   }
@@ -146,7 +146,7 @@ inline Status OptionalInsertControls(const Operation& op,
 }
 
 // series of fixed signature gate builders.
-// there is no need to error check for unparseable symbols
+// there is no need to error check for unparsable symbols
 // or proto args not being present. Those errors are caught
 // upstream.
 

tensorflow_quantum/core/src/circuit_parser_qsim.h

@@ -72,7 +72,7 @@ tensorflow::Status QsimCircuitFromProgram(
     const absl::flat_hash_map<std::string, std::pair<int, float>>& param_map,
     const int num_qubits, qsim::Circuit<qsim::Cirq::GateCirq<float>>* circuit,
     std::vector<qsim::GateFused<qsim::Cirq::GateCirq<float>>>* fused_circuit,
-    std::vector<GateMetaData>* metdata = nullptr);
+    std::vector<GateMetaData>* metadata = nullptr);
 
 // parse a serialized Cirq program into a qsim representation.
 // ingests a Cirq Circuit proto and produces a resolved Noisy qsim Circuit.

tensorflow_quantum/core/src/circuit_parser_qsim_test.cc

@@ -1125,7 +1125,7 @@ TEST(QsimCircuitParserTest, InvalidControlValues) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
             tensorflow::Status(absl::StatusCode::kInvalidArgument,
-                               "Unparseable control value: junk"));
+                               "Unparsable control value: junk"));
 }
 
 TEST(QsimCircuitParserTest, MismatchControlNum) {
@@ -1158,7 +1158,7 @@ TEST(QsimCircuitParserTest, MismatchControlNum) {
                                    &fused_circuit, &metadata),
             tensorflow::Status(
                 absl::StatusCode::kInvalidArgument,
-                "Mistmatched number of control qubits and control values."));
+                "Mismatched number of control qubits and control values."));
 }
 
 TEST(QsimCircuitParserTest, EmptyTest) {