Bumped Version to cirq 0.8. (tensorflow#234)

* Bumped Version to cirq 0.8.

* format.

* oops 0.9

* pinned sympy.

* Fixed notebooks.

* pinned sympy in setup as well.

docs/tutorials/barren_plateaus.ipynb

@@ -199,7 +199,7 @@
         "    \"\"\"Generate random QNN's with the same structure from McClean et al.\"\"\"\n",
         "    circuit = cirq.Circuit()\n",
         "    for qubit in qubits:\n",
-        "        circuit += cirq.Ry(np.pi / 4.0)(qubit)\n",
+        "        circuit += cirq.ry(np.pi / 4.0)(qubit)\n",
         "\n",
         "    for d in range(depth):\n",
         "        # Add a series of single qubit rotations.\n",
@@ -209,13 +209,13 @@
         "            ) * 2.0 * np.pi if i != 0 or d != 0 else symbol\n",
         "            if random_n > 2. / 3.:\n",
         "                # Add a Z.\n",
-        "                circuit += cirq.Rz(random_rot)(qubit)\n",
+        "                circuit += cirq.rz(random_rot)(qubit)\n",
         "            elif random_n > 1. / 3.:\n",
         "                # Add a Y.\n",
-        "                circuit += cirq.Ry(random_rot)(qubit)\n",
+        "                circuit += cirq.ry(random_rot)(qubit)\n",
         "            else:\n",
         "                # Add a X.\n",
-        "                circuit += cirq.Rx(random_rot)(qubit)\n",
+        "                circuit += cirq.rx(random_rot)(qubit)\n",
         "\n",
         "        # Add CZ ladder.\n",
         "        for src, dest in zip(qubits, qubits[1:]):\n",

docs/tutorials/qcnn.ipynb

@@ -289,7 +289,7 @@
         "\n",
         "<img src=\"./images/qcnn_2.png\" width=\"1000\">\n",
         "\n",
-        "An \"excited\" cluster state is defined as a cluster state that had a `cirq.Rx` gate applied to any of its qubits. Qconv and QPool are discussed later in this tutorial."
+        "An \"excited\" cluster state is defined as a cluster state that had a `cirq.rx` gate applied to any of its qubits. Qconv and QPool are discussed later in this tutorial."
       ]
     },
     {
@@ -318,7 +318,7 @@
       },
       "source": [
         "### 1.4 Data\n",
-        "Before building your model, you can generate your data. In this case it's going to be excitations to the cluster state (The original paper uses a more complicated dataset). Excitations are represented with `cirq.Rx` gates. A large enough rotation is deemed an excitation and is labeled `1` and a rotation that isn't large enough is labeled `-1` and deemed not an excitation."
+        "Before building your model, you can generate your data. In this case it's going to be excitations to the cluster state (The original paper uses a more complicated dataset). Excitations are represented with `cirq.rx` gates. A large enough rotation is deemed an excitation and is labeled `1` and a rotation that isn't large enough is labeled `-1` and deemed not an excitation."
       ]
     },
     {
@@ -339,7 +339,7 @@
         "    for n in range(n_rounds):\n",
         "        for bit in qubits:\n",
         "            rng = np.random.uniform(-np.pi, np.pi)\n",
-        "            excitations.append(cirq.Circuit(cirq.Rx(rng)(bit)))\n",
+        "            excitations.append(cirq.Circuit(cirq.rx(rng)(bit)))\n",
         "            labels.append(1 if (-np.pi / 2) <= rng <= (np.pi / 2) else -1)\n",
         "\n",
         "    split_ind = int(len(excitations) * 0.7)\n",

release/setup.py

@@ -50,7 +50,7 @@ def finalize_options(self):
             self.install_lib = self.install_platlib
 
 
-REQUIRED_PACKAGES = ['cirq == 0.7.0', 'pathos == 0.2.5']
+REQUIRED_PACKAGES = ['cirq == 0.8.0', 'pathos == 0.2.5', 'sympy == 1.4']
 CUR_VERSION = '0.3.0'
 
 

requirements.txt

@@ -1,4 +1,5 @@
-cirq==0.7.0
+cirq==0.8.0
+sympy==1.4
 nbconvert==5.6.1
 nbformat==4.4.0
 pylint==2.4.4

tensorflow_quantum/core/ops/tfq_ps_util_ops_test.py

@@ -671,7 +671,7 @@ def test_empty(self):
     def test_rotation_gates(self):
         """Test that rotation gates work."""
         bit = cirq.GridQubit(0, 0)
-        circuit = cirq.Circuit(cirq.Rx(sympy.Symbol('alpha') * 5.0)(bit))
+        circuit = cirq.Circuit(cirq.rx(sympy.Symbol('alpha') * 5.0)(bit))
         inputs = util.convert_to_tensor([circuit])
         symbols = tf.convert_to_tensor(['alpha'])
         res = tfq_ps_util_ops.tfq_ps_weights_from_symbols(inputs, symbols)

tensorflow_quantum/core/serialize/serializer.py

@@ -46,7 +46,7 @@ def _scalar_extractor(x):
     In the future we should likely get rid of this in favor of proper
     expression parsing once cirq supports it. See cirq.op_serializer
     and cirq's program protobuf for details. This is needed for things
-    like cirq.Rx('alpha').
+    like cirq.rx('alpha').
     """
     if not isinstance(x, (numbers.Real, sympy.Expr)):
         raise TypeError("Invalid input argument for exponent.")
@@ -111,14 +111,15 @@ def _eigen_gate_serializer(gate_type, serialized_id):
         cirq.google.SerializingArg(
             serialized_name="exponent",
             serialized_type=float,
-            gate_getter=lambda x: _symbol_extractor(x.exponent)),
+            op_getter=lambda x: _symbol_extractor(x.gate.exponent)),
         cirq.google.SerializingArg(
             serialized_name="exponent_scalar",
             serialized_type=float,
-            gate_getter=lambda x: _scalar_extractor(x.exponent)),
-        cirq.google.SerializingArg(serialized_name="global_shift",
-                                   serialized_type=float,
-                                   gate_getter=lambda x: float(x._global_shift))
+            op_getter=lambda x: _scalar_extractor(x.gate.exponent)),
+        cirq.google.SerializingArg(
+            serialized_name="global_shift",
+            serialized_type=float,
+            op_getter=lambda x: float(x.gate._global_shift))
     ]
     return cirq.google.GateOpSerializer(gate_type=gate_type,
                                         serialized_gate_id=serialized_id,
@@ -134,7 +135,7 @@ def _scalar_combiner(exponent, global_shift, exponent_scalar):
         In the future we should likely get rid of this in favor of proper
         expression parsing once cirq supports it. See cirq.op_serializer
         and cirq's program protobuf for details. This is needed for things
-        like cirq.Rx('alpha').
+        like cirq.rx('alpha').
         """
         if exponent_scalar == 1.0:
             return gate_type(exponent=exponent, global_shift=global_shift)
@@ -161,19 +162,19 @@ def _fsim_gate_serializer():
         cirq.google.SerializingArg(
             serialized_name="theta",
             serialized_type=float,
-            gate_getter=lambda x: _symbol_extractor(x.theta)),
+            op_getter=lambda x: _symbol_extractor(x.gate.theta)),
         cirq.google.SerializingArg(
             serialized_name="phi",
             serialized_type=float,
-            gate_getter=lambda x: _symbol_extractor(x.phi)),
+            op_getter=lambda x: _symbol_extractor(x.gate.phi)),
         cirq.google.SerializingArg(
             serialized_name="theta_scalar",
             serialized_type=float,
-            gate_getter=lambda x: _scalar_extractor(x.theta)),
+            op_getter=lambda x: _scalar_extractor(x.gate.theta)),
         cirq.google.SerializingArg(
             serialized_name="phi_scalar",
             serialized_type=float,
-            gate_getter=lambda x: _scalar_extractor(x.phi)),
+            op_getter=lambda x: _scalar_extractor(x.gate.phi)),
     ]
     return cirq.google.GateOpSerializer(gate_type=cirq.FSimGate,
                                         serialized_gate_id="FSIM",
@@ -209,7 +210,7 @@ def _identity_gate_serializer():
     """Make a standard serializer for the single qubit identity."""
 
     def _identity_check(x):
-        if x.num_qubits() != 1:
+        if x.gate.num_qubits() != 1:
             raise ValueError("Multi-Qubit identity gate not supported."
                              "Given: {}. To work around this, use "
                              "cirq.I.on_each instead.".format(str(x)))
@@ -220,7 +221,7 @@ def _identity_check(x):
     args = [
         cirq.google.SerializingArg(serialized_name="unused",
                                    serialized_type=bool,
-                                   gate_getter=_identity_check)
+                                   op_getter=_identity_check)
     ]
     return cirq.google.GateOpSerializer(gate_type=cirq.IdentityGate,
                                         serialized_gate_id="I",
@@ -250,22 +251,23 @@ def _phased_eigen_gate_serializer(gate_type, serialized_id):
         cirq.google.SerializingArg(
             serialized_name="phase_exponent",
             serialized_type=float,
-            gate_getter=lambda x: _symbol_extractor(x.phase_exponent)),
+            op_getter=lambda x: _symbol_extractor(x.gate.phase_exponent)),
         cirq.google.SerializingArg(
             serialized_name="phase_exponent_scalar",
             serialized_type=float,
-            gate_getter=lambda x: _scalar_extractor(x.phase_exponent)),
+            op_getter=lambda x: _scalar_extractor(x.gate.phase_exponent)),
         cirq.google.SerializingArg(
             serialized_name="exponent",
             serialized_type=float,
-            gate_getter=lambda x: _symbol_extractor(x.exponent)),
+            op_getter=lambda x: _symbol_extractor(x.gate.exponent)),
         cirq.google.SerializingArg(
             serialized_name="exponent_scalar",
             serialized_type=float,
-            gate_getter=lambda x: _scalar_extractor(x.exponent)),
-        cirq.google.SerializingArg(serialized_name="global_shift",
-                                   serialized_type=float,
-                                   gate_getter=lambda x: float(x.global_shift))
+            op_getter=lambda x: _scalar_extractor(x.gate.exponent)),
+        cirq.google.SerializingArg(
+            serialized_name="global_shift",
+            serialized_type=float,
+            op_getter=lambda x: float(x.gate.global_shift))
     ]
     return cirq.google.GateOpSerializer(gate_type=gate_type,
                                         serialized_gate_id=serialized_id,
@@ -282,7 +284,7 @@ def _scalar_combiner(exponent, global_shift, exponent_scalar,
         In the future we should likely get rid of this in favor of proper
         expression parsing once cirq supports it. See cirq.op_serializer
         and cirq's program protobuf for details. This is needed for things
-        like cirq.Rx('alpha').
+        like cirq.rx('alpha').
         """
         # Do this to help with rounding. it's ugly.
         exponent = exponent if exponent_scalar == 1.0 \

tensorflow_quantum/core/serialize/serializer_test.py

@@ -346,15 +346,15 @@ def _get_valid_circuit_proto_pairs():
         # RX, RY, RZ with symbolization is tested in special cases as the
         # string comparison of the float converted sympy.pi does not happen
         # smoothly. See: test_serialize_deserialize_special_case_one_qubit
-        (cirq.Circuit(cirq.Rx(np.pi)(q0)),
+        (cirq.Circuit(cirq.rx(np.pi)(q0)),
          _build_gate_proto("XP",
                            ['exponent', 'exponent_scalar', 'global_shift'],
                            [1.0, 1.0, -0.5], ['0_0'])),
-        (cirq.Circuit(cirq.Ry(np.pi)(q0)),
+        (cirq.Circuit(cirq.ry(np.pi)(q0)),
          _build_gate_proto("YP",
                            ['exponent', 'exponent_scalar', 'global_shift'],
                            [1.0, 1.0, -0.5], ['0_0'])),
-        (cirq.Circuit(cirq.Rz(np.pi)(q0)),
+        (cirq.Circuit(cirq.rz(np.pi)(q0)),
          _build_gate_proto("ZP",
                            ['exponent', 'exponent_scalar', 'global_shift'],
                            [1.0, 1.0, -0.5], ['0_0'])),
@@ -591,13 +591,13 @@ def test_serialize_deserialize_paulisum_consistency(self, sum_proto_pair):
 
     @parameterized.parameters([
         {
-            'gate': cirq.Rx(3.0 * sympy.Symbol('alpha'))
+            'gate': cirq.rx(3.0 * sympy.Symbol('alpha'))
         },
         {
-            'gate': cirq.Ry(-1.0 * sympy.Symbol('alpha'))
+            'gate': cirq.ry(-1.0 * sympy.Symbol('alpha'))
         },
         {
-            'gate': cirq.Rz(sympy.Symbol('alpha'))
+            'gate': cirq.rz(sympy.Symbol('alpha'))
         },
     ])
     def test_serialize_deserialize_special_case_one_qubit(self, gate):

tensorflow_quantum/python/differentiators/parameter_shift_util_test.py

@@ -48,7 +48,7 @@ def test_parse_programs(self):
         q = cirq.GridQubit.rect(1, n_qubits)
         c = cirq.Circuit()
         c.append([
-            cirq.Rz(coeff[i] * sympy_symbols[i % 2]).on(q[i])
+            cirq.rz(coeff[i] * sympy_symbols[i % 2]).on(q[i])
             for i in range(n_qubits)
         ])
         circuit_batch = [c] * n_programs

tensorflow_quantum/python/differentiators/stochastic_differentiator_util_test.py

@@ -34,7 +34,7 @@ def _example_circuit_helper(n_qubits, n_programs):
     coeff = [1.0, -2.0, 3.0, -4.0, 5.0]
     q = cirq.GridQubit.rect(1, n_qubits)
     c = cirq.Circuit([
-        cirq.Rz(coeff[i] * sympy_symbols[i % 2]).on(q[i])
+        cirq.rz(coeff[i] * sympy_symbols[i % 2]).on(q[i])
         for i in range(n_qubits)
     ])
     circuit_batch = [c] * n_programs

tensorflow_quantum/python/layers/circuit_executors/expectation.py

@@ -38,12 +38,12 @@ class Expectation(tf.keras.layers.Layer):
     ...     \"""Generate a toy problem on 1 qubit.\"""
     ...     starting_state = [0.123, 0.456, 0.789]
     ...     circuit = cirq.Circuit(
-    ...         cirq.Rx(starting_state[0])(bit),
-    ...         cirq.Ry(starting_state[1])(bit),
-    ...         cirq.Rz(starting_state[2])(bit),
-    ...         cirq.Rz(symbols[2])(bit),
-    ...         cirq.Ry(symbols[1])(bit),
-    ...         cirq.Rx(symbols[0])(bit)
+    ...         cirq.rx(starting_state[0])(bit),
+    ...         cirq.ry(starting_state[1])(bit),
+    ...         cirq.rz(starting_state[2])(bit),
+    ...         cirq.rz(symbols[2])(bit),
+    ...         cirq.ry(symbols[1])(bit),
+    ...         cirq.rx(symbols[0])(bit)
     ...     )
     ...     return circuit
 

tensorflow_quantum/python/layers/circuit_executors/expectation_test.py

@@ -27,12 +27,12 @@ def _gen_single_bit_rotation_problem(bit, symbols):
     """Generate a toy problem on 1 qubit."""
     starting_state = np.random.uniform(0, 2 * np.pi, 3)
     circuit = cirq.Circuit(
-        cirq.Rx(starting_state[0])(bit),
-        cirq.Ry(starting_state[1])(bit),
-        cirq.Rz(starting_state[2])(bit),
-        cirq.Rz(symbols[2])(bit),
-        cirq.Ry(symbols[1])(bit),
-        cirq.Rx(symbols[0])(bit))
+        cirq.rx(starting_state[0])(bit),
+        cirq.ry(starting_state[1])(bit),
+        cirq.rz(starting_state[2])(bit),
+        cirq.rz(symbols[2])(bit),
+        cirq.ry(symbols[1])(bit),
+        cirq.rx(symbols[0])(bit))
 
     return circuit
 
@@ -170,7 +170,7 @@ def test_expectation_simple_tf_train(self):
         """
         bit = cirq.GridQubit(0, 0)
         circuit = \
-            cirq.Circuit(cirq.Rx(sympy.Symbol('theta'))(bit))
+            cirq.Circuit(cirq.rx(sympy.Symbol('theta'))(bit))
         op = cirq.Z(bit)
         layer = expectation.Expectation()
         optimizer = tf.optimizers.Adam(learning_rate=0.05)

tensorflow_quantum/python/layers/circuit_executors/sampled_expectation.py

@@ -41,12 +41,12 @@ class SampledExpectation(tf.keras.layers.Layer):
     ...     \"""Generate a toy problem on 1 qubit.\"""
     ...     starting_state = [0.123, 0.456, 0.789]
     ...     circuit = cirq.Circuit(
-    ...         cirq.Rx(starting_state[0])(bit),
-    ...         cirq.Ry(starting_state[1])(bit),
-    ...         cirq.Rz(starting_state[2])(bit),
-    ...         cirq.Rz(symbols[2])(bit),
-    ...         cirq.Ry(symbols[1])(bit),
-    ...         cirq.Rx(symbols[0])(bit)
+    ...         cirq.rx(starting_state[0])(bit),
+    ...         cirq.ry(starting_state[1])(bit),
+    ...         cirq.rz(starting_state[2])(bit),
+    ...         cirq.rz(symbols[2])(bit),
+    ...         cirq.ry(symbols[1])(bit),
+    ...         cirq.rx(symbols[0])(bit)
     ...     )
     ...     return circuit
 

tensorflow_quantum/python/layers/circuit_executors/sampled_expectation_test.py

@@ -29,12 +29,12 @@ def _gen_single_bit_rotation_problem(bit, symbols):
     """Generate a toy problem on 1 qubit."""
     starting_state = np.random.uniform(0, 2 * np.pi, 3)
     circuit = cirq.Circuit(
-        cirq.Rx(starting_state[0])(bit),
-        cirq.Ry(starting_state[1])(bit),
-        cirq.Rz(starting_state[2])(bit),
-        cirq.Rz(symbols[2])(bit),
-        cirq.Ry(symbols[1])(bit),
-        cirq.Rx(symbols[0])(bit))
+        cirq.rx(starting_state[0])(bit),
+        cirq.ry(starting_state[1])(bit),
+        cirq.rz(starting_state[2])(bit),
+        cirq.rz(symbols[2])(bit),
+        cirq.ry(symbols[1])(bit),
+        cirq.rx(symbols[0])(bit))
 
     return circuit
 
@@ -227,7 +227,7 @@ def test_static_cases(self):
     def test_sampled_expectation_simple_tf_train(self):
         """Train a layer using standard tf (not keras)."""
         bit = cirq.GridQubit(0, 0)
-        circuit = cirq.Circuit(cirq.Rx(sympy.Symbol('theta'))(bit))
+        circuit = cirq.Circuit(cirq.rx(sympy.Symbol('theta'))(bit))
         layer = sampled_expectation.SampledExpectation()
         optimizer = tf.optimizers.Adam(learning_rate=0.05)
         for _ in range(10):

tensorflow_quantum/python/layers/high_level/controlled_pqc_test.py

@@ -142,7 +142,7 @@ def test_controlled_pqc_simple_learn(self, backend, repetitions):
         on many backends."""
         bit = cirq.GridQubit(0, 0)
         circuit = \
-            cirq.Circuit(cirq.Rx(sympy.Symbol('theta'))(bit))
+            cirq.Circuit(cirq.rx(sympy.Symbol('theta'))(bit))
 
         inputs = tf.keras.Input(shape=(1,), dtype=tf.dtypes.float32)
         quantum_datum = tf.keras.Input(shape=(), dtype=tf.dtypes.string)

tensorflow_quantum/python/layers/high_level/pqc.py

@@ -40,11 +40,11 @@ class PQC(tf.keras.layers.Layer):
     >>> q = cirq.GridQubit(0, 0)
     >>> (a, b, c) = sympy.symbols("a b c")
     >>> circuit = cirq.Circuit(
-    ...     cirq.Rz(a)(q),
-    ...     cirq.Rx(b)(q),
-    ...     cirq.Rz(c)(q),
-    ...     cirq.Rx(-b)(q),
-    ...     cirq.Rz(-a)(q)
+    ...     cirq.rz(a)(q),
+    ...     cirq.rx(b)(q),
+    ...     cirq.rz(c)(q),
+    ...     cirq.rx(-b)(q),
+    ...     cirq.rz(-a)(q)
     ... )
 
 
@@ -101,11 +101,11 @@ class PQC(tf.keras.layers.Layer):
     >>> q = cirq.GridQubit(0, 0)
     >>> (a, b, c) = sympy.symbols("a b c")
     >>> circuit = cirq.Circuit(
-    ...     cirq.Rz(a)(q),
-    ...     cirq.Rx(b)(q),
-    ...     cirq.Rz(c)(q),
-    ...     cirq.Rx(-b)(q),
-    ...     cirq.Rz(-a)(q)
+    ...     cirq.rz(a)(q),
+    ...     cirq.rx(b)(q),
+    ...     cirq.rz(c)(q),
+    ...     cirq.rx(-b)(q),
+    ...     cirq.rz(-a)(q)
     ... )
     >>> measurement = [cirq.X(q), cirq.Y(q), cirq.Z(q)]
     >>> outputs = tfq.layers.PQC(