Update deprecated Cirq names (wavefunction -> state_vector) (#403)

* updated some wavefunction to state_vector

* removed remaining instances of wavefunction, replaced with state_vector

* changed final_state to final_state_vector

* formatting

* formatting

Author: Antonio Martinez

docs/tutorials/gradients.ipynb

@@ -233,8 +233,8 @@
         "    \"\"\"Compute ⟨Y(alpha)| `op` | Y(alpha)⟩\"\"\"\n",
         "    params = {'alpha': alpha}\n",
         "    sim = cirq.Simulator()\n",
-        "    final_state = sim.simulate(my_circuit, params).final_state\n",
-        "    return op.expectation_from_wavefunction(final_state, {qubit: 0}).real\n",
+        "    final_state_vector = sim.simulate(my_circuit, params).final_state_vector\n",
+        "    return op.expectation_from_state_vector(final_state_vector, {qubit: 0}).real\n",
         "\n",
         "\n",
         "my_alpha = 0.3\n",

docs/tutorials/hello_many_worlds.ipynb

@@ -246,7 +246,7 @@
       "source": [
         "# Calculate a state vector with a=0.5 and b=-0.5.\n",
         "resolver = cirq.ParamResolver({a: 0.5, b: -0.5})\n",
-        "output_state_vector = cirq.Simulator().simulate(circuit, resolver).final_state\n",
+        "output_state_vector = cirq.Simulator().simulate(circuit, resolver).final_state_vector\n",
         "output_state_vector"
       ]
     },
@@ -275,7 +275,7 @@
         "\n",
         "qubit_map={q0: 0, q1: 1}\n",
         "\n",
-        "z0.expectation_from_wavefunction(output_state_vector, qubit_map).real"
+        "z0.expectation_from_state_vector(output_state_vector, qubit_map).real"
       ]
     },
     {
@@ -290,7 +290,7 @@
       "source": [
         "z0x1 = 0.5 * z0 + cirq.X(q1)\n",
         "\n",
-        "z0x1.expectation_from_wavefunction(output_state_vector, qubit_map).real"
+        "z0x1.expectation_from_state_vector(output_state_vector, qubit_map).real"
       ]
     },
     {
@@ -402,9 +402,9 @@
         "\n",
         "for vals in batch_vals:\n",
         "    resolver = cirq.ParamResolver({a: vals[0], b: vals[1]})\n",
-        "    final_state = cirq_simulator.simulate(circuit, resolver).final_state\n",
+        "    final_state_vector = cirq_simulator.simulate(circuit, resolver).final_state_vector\n",
         "    cirq_results.append(\n",
-        "        [z0.expectation_from_wavefunction(final_state, {\n",
+        "        [z0.expectation_from_state_vector(final_state_vector, {\n",
         "            q0: 0,\n",
         "            q1: 1\n",
         "        }).real])\n",
@@ -919,8 +919,8 @@
         "    state = cirq_simulator.simulate(\n",
         "        full_circuit,\n",
         "        {s:v for (s,v) in zip(control_params, params_to_prepare_output[index])}\n",
-        "    ).final_state\n",
-        "    expectation = z0.expectation_from_wavefunction(state, {qubit: 0}).real\n",
+        "    ).final_state_vector\n",
+        "    expectation = z0.expectation_from_state_vector(state, {qubit: 0}).real\n",
         "    print(f'For a desired output (expectation) of {desired_values[index]} with'\n",
         "          f' noisy preparation, the controller\\nnetwork found the following '\n",
         "          f'values for theta: {params_to_prepare_output[index]}\\nWhich gives an'\n",

tensorflow_quantum/core/ops/batch_util.py

@@ -211,7 +211,7 @@ def _setup_dict(array_view, view_shape, simulator, post_process):
 
 
 def _state_worker_func(indices, programs, params):
-    """Compute the wavefunction for each program in indices."""
+    """Compute the state vector for each program in indices."""
     x_np = _convert_complex_view_to_np(INFO_DICT['arr'], INFO_DICT['shape'])
     simulator = INFO_DICT['sim']
 
@@ -243,7 +243,7 @@ def _analytical_expectation_worker_func(indices, programs, params, ops):
             continue
 
         if old_batch_index != batch_index:
-            # must compute a new wavefunction.
+            # must compute a new state vector.
             qubit_oder = dict(
                 zip(sorted(programs[batch_index].all_qubits()),
                     list(range(len(programs[batch_index].all_qubits())))))
@@ -349,7 +349,7 @@ def batch_calculate_state(circuits, param_resolvers, simulator):
     `cirq.ParamResolver` in `param_resolvers` was used to resolve any symbols
     in it. If simulator is a `cirq.DensityMatrixSimulator` this final state will
     be a density matrix, if simulator is a `cirq.Simulator` this final state
-    will be a wavefunction. More specifically for a given `i`
+    will be a state vector. More specifically for a given `i`
     `batch_calculate_state` will use `param_resolvers[i]` to resolve the symbols
     in `circuits[i]` and then place the final state in the return list at index
     `i`.
@@ -445,8 +445,8 @@ def batch_calculate_expectation(circuits, param_resolvers, ops, simulator):
                 state.final_density_matrix, order) for x in op).real
     elif isinstance(simulator, cirq.Simulator):
         post_process = \
-            lambda op, state, order: op.expectation_from_wavefunction(
-                state.final_state, order).real
+            lambda op, state, order: op.expectation_from_state_vector(
+                state.final_state_vector, order).real
     else:
         raise TypeError('Simulator {} is not supported by '
                         'batch_calculate_expectation.'.format(type(simulator)))
@@ -620,7 +620,7 @@ def batch_sample(circuits, param_resolvers, n_samples, simulator):
                 repetitions=n_samples)
     elif isinstance(simulator, cirq.Simulator):
         post_process = lambda state, size, n_samples: cirq.sample_state_vector(
-            state.final_state, list(range(size)), repetitions=n_samples)
+            state.final_state_vector, list(range(size)), repetitions=n_samples)
     else:
         raise TypeError('Simulator {} is not supported by batch_sample.'.format(
             type(simulator)))

tensorflow_quantum/core/ops/batch_util_test.py

@@ -37,7 +37,7 @@ def _get_mixed_batch(qubits, symbols, size):
 
 def _pad_state(sim, state, n):
     if isinstance(sim, cirq.Simulator):
-        state = state.final_state
+        state = state.final_state_vector
     if isinstance(sim, cirq.DensityMatrixSimulator):
         state = state.final_density_matrix
     return np.pad(state, (0, (1 << n) - state.shape[-1]),
@@ -47,9 +47,10 @@ def _pad_state(sim, state, n):
 
 def _expectation_helper(sim, circuit, params, op):
     if isinstance(sim, cirq.Simulator):
-        state = sim.simulate(circuit, params).final_state.astype(np.complex128)
+        state = sim.simulate(circuit,
+                             params).final_state_vector.astype(np.complex128)
         return [
-            op.expectation_from_wavefunction(
+            op.expectation_from_state_vector(
                 state,
                 dict(
                     zip(sorted(circuit.all_qubits()),
@@ -73,7 +74,7 @@ def _expectation_helper(sim, circuit, params, op):
 
 def _sample_helper(sim, state, n_qubits, n_samples):
     if isinstance(sim, cirq.Simulator):
-        return cirq.sample_state_vector(state.final_state,
+        return cirq.sample_state_vector(state.final_state_vector,
                                         list(range(n_qubits)),
                                         repetitions=n_samples)
     if isinstance(sim, cirq.DensityMatrixSimulator):
@@ -92,8 +93,8 @@ class BatchUtilTest(tf.test.TestCase, parameterized.TestCase):
     }, {
         'sim': cirq.Simulator()
     }])
-    def test_batch_simulate_state(self, sim):
-        """Test variable sized wavefunction output."""
+    def test_batch_simulate_state_vector(self, sim):
+        """Test variable sized state vector output."""
         circuit_batch, resolver_batch = _get_mixed_batch(
             cirq.GridQubit.rect(1, N_QUBITS), SYMBOLS, BATCH_SIZE)
         results = batch_util.batch_calculate_state(circuit_batch,

tensorflow_quantum/core/ops/circuit_execution_ops.py

@@ -22,7 +22,7 @@
 from tensorflow_quantum.python import quantum_context
 
 
-class TFQWavefunctionSimulator(enum.Enum):
+class TFQStateVectorSimulator(enum.Enum):
     """Enum to make specifying TFQ simulators user-friendly."""
     expectation = tfq_simulate_ops.tfq_simulate_expectation
     samples = tfq_simulate_ops.tfq_simulate_samples
@@ -120,7 +120,7 @@ def get_expectation_op(
 
     op = None
     if backend is None:
-        op = TFQWavefunctionSimulator.expectation
+        op = TFQStateVectorSimulator.expectation
 
     if isinstance(backend, cirq.SimulatesFinalState):
         op = cirq_ops._get_cirq_analytical_expectation(backend)
@@ -216,7 +216,7 @@ def get_sampling_op(
 
     op = None
     if backend is None:
-        op = TFQWavefunctionSimulator.samples
+        op = TFQStateVectorSimulator.samples
 
     if isinstance(backend, cirq.Sampler):
         op = cirq_ops._get_cirq_samples(backend)
@@ -269,7 +269,7 @@ def get_state_op(
         backend: Optional Python `object` that specifies what backend this op
             should use when evaluating circuits. Can be any
             `cirq.SimulatesFinalState`. If not provided, the default C++
-            wavefunction simulator will be used.
+            state vector simulator will be used.
         quantum_concurrent: Optional Python `bool`. True indicates that the
             returned op should not block graph level parallelism on itself when
             executing. False indicates that graph level parallelism on itself
@@ -305,7 +305,7 @@ def get_state_op(
 
     op = None
     if backend is None:
-        op = TFQWavefunctionSimulator.state
+        op = TFQStateVectorSimulator.state
 
     if isinstance(backend, (cirq.SimulatesFinalState)):
         op = cirq_ops._get_cirq_simulate_state(backend)
@@ -416,7 +416,7 @@ def get_sampled_expectation_op(
 
     op = None
     if backend is None:
-        op = TFQWavefunctionSimulator.sampled_expectation
+        op = TFQStateVectorSimulator.sampled_expectation
 
     if isinstance(backend, cirq.Sampler):
         op = cirq_ops._get_cirq_sampled_expectation(backend)

tensorflow_quantum/core/ops/cirq_ops_test.py

@@ -281,10 +281,10 @@ def test_simulate_state_output_padding(self, op_and_sim, all_n_qubits):
                 blank_state[:dm.shape[0], :dm.shape[1]] = dm
                 manual_padded_results.append(blank_state)
 
-            # wavefunctions should be zero everywhere to the right of the states
+            # state vectors should be zero everywhere to the right of the states
             # present in this system
             elif isinstance(result, cirq.StateVectorTrialResult):
-                wf = result.final_state
+                wf = result.final_state_vector
                 blank_state = np.ones(
                     (2**max(all_n_qubits)), dtype=np.complex64) * -2
                 blank_state[:wf.shape[0]] = wf

tensorflow_quantum/core/ops/math_ops/inner_product_op_test.py

@@ -245,9 +245,9 @@ def test_correctness_with_symbols(self, n_qubits, batch_size,
         for i in range(batch_size):
             final_circuit = cirq.resolve_parameters(circuit_batch[i],
                                                     resolver_batch[i])
-            final_wf = cirq.final_wavefunction(final_circuit)
+            final_wf = cirq.final_state_vector(final_circuit)
             for j in range(inner_dim_size):
-                internal_wf = cirq.final_wavefunction(other_batch[i][j])
+                internal_wf = cirq.final_state_vector(other_batch[i][j])
                 out_arr[i][j] = np.vdot(final_wf, internal_wf)
 
         self.assertAllClose(out, out_arr)
@@ -292,9 +292,9 @@ def test_correctness_without_symbols(self, n_qubits, batch_size,
 
         out_arr = np.empty((batch_size, inner_dim_size), dtype=np.complex64)
         for i in range(batch_size):
-            final_wf = cirq.final_wavefunction(circuit_batch[i])
+            final_wf = cirq.final_state_vector(circuit_batch[i])
             for j in range(inner_dim_size):
-                internal_wf = cirq.final_wavefunction(other_batch[i][j])
+                internal_wf = cirq.final_state_vector(other_batch[i][j])
                 out_arr[i][j] = np.vdot(final_wf, internal_wf)
 
         self.assertAllClose(out, out_arr)

tensorflow_quantum/core/ops/math_ops/tfq_inner_product.cc

@@ -162,7 +162,7 @@ class TfqInnerProductOp : public tensorflow::OpKernel {
     State sv = StateSpace(largest_nq, tfq_for).CreateState();
     State scratch = StateSpace(largest_nq, tfq_for).CreateState();
 
-    // Simulate programs one by one. Parallelizing over wavefunctions
+    // 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 necessary.
     for (int i = 0; i < fused_circuits.size(); i++) {
@@ -239,8 +239,8 @@ class TfqInnerProductOp : public tensorflow::OpKernel {
         }
 
         if (cur_batch_index != old_batch_index) {
-          // We've run into a new wavefunction we must compute.
-          // Only compute a new wavefunction when we have to.
+          // We've run into a new state vector we must compute.
+          // Only compute a new state vector when we have to.
           if (nq > largest_nq) {
             sv = ss.CreateState();
             scratch = ss.CreateState();

tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc

@@ -108,7 +108,7 @@ class TfqCalculateUnitaryOp : public tensorflow::OpKernel {
     int largest_nq = 1;
     Unitary u = UnitarySpace(largest_nq, tfq_for).CreateUnitary();
 
-    // Simulate programs one by one. Parallelizing over wavefunctions
+    // 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.
     for (int i = 0; i < fused_circuits.size(); i++) {

tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc

@@ -134,7 +134,7 @@ class TfqSimulateExpectationOp : public tensorflow::OpKernel {
     State sv = StateSpace(largest_nq, tfq_for).CreateState();
     State scratch = StateSpace(largest_nq, tfq_for).CreateState();
 
-    // Simulate programs one by one. Parallelizing over wavefunctions
+    // 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 necessary.
     for (int i = 0; i < fused_circuits.size(); i++) {
@@ -205,8 +205,8 @@ class TfqSimulateExpectationOp : public tensorflow::OpKernel {
         }
 
         if (cur_batch_index != old_batch_index) {
-          // We've run into a new wavefunction we must compute.
-          // Only compute a new wavefunction when we have to.
+          // We've run into a new state vector we must compute.
+          // Only compute a new state vector when we have to.
           if (nq > largest_nq) {
             sv = ss.CreateState();
             scratch = ss.CreateState();