Custom-state-representation simulator infra

cirq-core/cirq/contrib/custom_simulators/custom_state_simulator.py

@@ -0,0 +1,91 @@
+# Copyright 2022 The Cirq Developers
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from typing import Any, Dict, Generic, Sequence, Type, TYPE_CHECKING
+
+import numpy as np
+
+from cirq import sim
+from cirq.sim.simulation_state import TSimulationState
+
+if TYPE_CHECKING:
+    import cirq
+
+
+class CustomStateStepResult(sim.StepResultBase[TSimulationState], Generic[TSimulationState]):
+    """The step result provided by `CustomStateSimulator.simulate_moment_steps`."""
+
+    pass
+
+
+class CustomStateTrialResult(
+    sim.SimulationTrialResultBase[TSimulationState], Generic[TSimulationState]
+):
+    """The trial result provided by `CustomStateSimulator.simulate`."""
+
+    pass
+
+
+class CustomStateSimulator(
+    sim.SimulatorBase[
+        CustomStateStepResult[TSimulationState],
+        CustomStateTrialResult[TSimulationState],
+        TSimulationState,
+    ],
+    Generic[TSimulationState],
+):
+    """A simulator that can be used to simulate custom states."""
+
+    def __init__(
+        self,
+        state_type: Type[TSimulationState],
+        *,
+        noise: 'cirq.NOISE_MODEL_LIKE' = None,
+        split_untangled_states: bool = False,
+    ):
+        """Initializes a CustomStateSimulator.
+
+        Args:
+            state_type: The class that represents the simulation state this simulator should use.
+            noise: The noise model used by the simulator.
+            split_untangled_states: True to run the simulation as a product state. This is only
+                supported if the `state_type` supports it via an implementation of `kron` and
+                `factor` methods. Otherwise a runtime error will occur during simulation."""
+        super().__init__(noise=noise, split_untangled_states=split_untangled_states)
+        self.state_type = state_type
+
+    def _create_simulator_trial_result(
+        self,
+        params: 'cirq.ParamResolver',
+        measurements: Dict[str, np.ndarray],
+        final_simulator_state: 'cirq.SimulationStateBase[TSimulationState]',
+    ) -> 'CustomStateTrialResult[TSimulationState]':
+        return CustomStateTrialResult(
+            params, measurements, final_simulator_state=final_simulator_state
+        )
+
+    def _create_step_result(
+        self, sim_state: 'cirq.SimulationStateBase[TSimulationState]'
+    ) -> 'CustomStateStepResult[TSimulationState]':
+        return CustomStateStepResult(sim_state)
+
+    def _create_partial_simulation_state(
+        self,
+        initial_state: Any,
+        qubits: Sequence['cirq.Qid'],
+        classical_data: 'cirq.ClassicalDataStore',
+    ) -> TSimulationState:
+        return self.state_type(
+            initial_state=initial_state, qubits=qubits, classical_data=classical_data
+        )

cirq-core/cirq/contrib/custom_simulators/custom_state_simulator_test.py

@@ -0,0 +1,192 @@
+# Copyright 2022 The Cirq Developers
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from typing import List, Sequence, Tuple
+
+import numpy as np
+import sympy
+
+import cirq
+from cirq.contrib.custom_simulators.custom_state_simulator import CustomStateSimulator
+
+
+class ComputationalBasisState(cirq.qis.QuantumStateRepresentation):
+    def __init__(self, initial_state: List[int]):
+        self.basis = initial_state
+
+    def copy(self, deep_copy_buffers: bool = True) -> 'ComputationalBasisState':
+        return ComputationalBasisState(self.basis)
+
+    def measure(self, axes: Sequence[int], seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None):
+        return [self.basis[i] for i in axes]
+
+
+class ComputationalBasisSimState(cirq.SimulationState[ComputationalBasisState]):
+    def __init__(self, initial_state, qubits, classical_data):
+        state = ComputationalBasisState(
+            cirq.big_endian_int_to_bits(initial_state, bit_count=len(qubits))
+        )
+        super().__init__(state=state, qubits=qubits, classical_data=classical_data)
+
+    def _act_on_fallback_(self, action, qubits: Sequence[cirq.Qid], allow_decompose: bool = True):
+        gate = action.gate if isinstance(action, cirq.Operation) else action
+        if isinstance(gate, cirq.XPowGate):
+            i = self.qubit_map[qubits[0]]
+            self._state.basis[i] = int(gate.exponent + self._state.basis[i]) % qubits[0].dimension
+            return True
+        pass
+
+
+def create_test_circuit():
+    q0, q1 = cirq.LineQid.range(2, dimension=3)
+    x = cirq.XPowGate(dimension=3)
+    return cirq.Circuit(
+        x(q0),
+        cirq.measure(q0, key='a'),
+        x(q0).with_classical_controls('a'),
+        cirq.CircuitOperation(
+            cirq.FrozenCircuit(x(q1), cirq.measure(q1, key='b')),
+            repeat_until=cirq.SympyCondition(sympy.Eq(sympy.Symbol('b'), 2)),
+            use_repetition_ids=False,
+        ),
+    )
+
+
+def test_basis_state_simulator():
+    sim = CustomStateSimulator(ComputationalBasisSimState)
+    circuit = create_test_circuit()
+    r = sim.simulate(circuit)
+    assert r.measurements == {'a': np.array([1]), 'b': np.array([2])}
+    assert r._final_simulator_state._state.basis == [2, 2]
+
+
+def test_built_in_states():
+    # Verify this works for the built-in states too, you just lose the custom step/trial results.
+    sim = CustomStateSimulator(cirq.StateVectorSimulationState)
+    circuit = create_test_circuit()
+    r = sim.simulate(circuit)
+    assert r.measurements == {'a': np.array([1]), 'b': np.array([2])}
+    assert np.allclose(
+        r._final_simulator_state._state._state_vector, [[0, 0, 0], [0, 0, 0], [0, 0, 1]]
+    )
+
+
+def test_product_state_mode_built_in_state():
+    sim = CustomStateSimulator(cirq.StateVectorSimulationState, split_untangled_states=True)
+    circuit = create_test_circuit()
+    r = sim.simulate(circuit)
+    assert r.measurements == {'a': np.array([1]), 'b': np.array([2])}
+
+    # Ensure the state is in product-state mode, and it's got three states (q0, q1, phase)
+    assert isinstance(r._final_simulator_state, cirq.SimulationProductState)
+    assert len(r._final_simulator_state.sim_states) == 3
+
+    assert np.allclose(
+        r._final_simulator_state.create_merged_state()._state._state_vector,
+        [[0, 0, 0], [0, 0, 0], [0, 0, 1]],
+    )
+
+
+def test_noise():
+    x = cirq.XPowGate(dimension=3)
+    sim = CustomStateSimulator(ComputationalBasisSimState, noise=x**2)
+    circuit = create_test_circuit()
+    r = sim.simulate(circuit)
+    assert r.measurements == {'a': np.array([2]), 'b': np.array([2])}
+    assert r._final_simulator_state._state.basis == [1, 2]
+
+
+def test_run():
+    sim = CustomStateSimulator(ComputationalBasisSimState)
+    circuit = create_test_circuit()
+    r = sim.run(circuit)
+    assert np.allclose(r.records['a'], np.array([[1]]))
+    assert np.allclose(r.records['b'], np.array([[1], [2]]))
+
+
+def test_parameterized_repetitions():
+    q = cirq.LineQid(0, dimension=5)
+    x = cirq.XPowGate(dimension=5)
+    circuit = cirq.Circuit(
+        cirq.CircuitOperation(
+            cirq.FrozenCircuit(x(q), cirq.measure(q, key='a')),
+            repetitions=sympy.Symbol('r'),
+            use_repetition_ids=False,
+        )
+    )
+
+    sim = CustomStateSimulator(ComputationalBasisSimState)
+    r = sim.run_sweep(circuit, [{'r': i} for i in range(1, 5)])
+    assert np.allclose(r[0].records['a'], np.array([[1]]))
+    assert np.allclose(r[1].records['a'], np.array([[1], [2]]))
+    assert np.allclose(r[2].records['a'], np.array([[1], [2], [3]]))
+    assert np.allclose(r[3].records['a'], np.array([[1], [2], [3], [4]]))
+
+
+class ComputationalBasisProductState(cirq.qis.QuantumStateRepresentation):
+    def __init__(self, initial_state: List[int]):
+        self.basis = initial_state
+
+    def copy(self, deep_copy_buffers: bool = True) -> 'ComputationalBasisProductState':
+        return ComputationalBasisProductState(self.basis)
+
+    def measure(self, axes: Sequence[int], seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None):
+        return [self.basis[i] for i in axes]
+
+    def kron(self, other: 'ComputationalBasisProductState') -> 'ComputationalBasisProductState':
+        return ComputationalBasisProductState(self.basis + other.basis)
+
+    def factor(
+        self, axes: Sequence[int], *, validate=True, atol=1e-07
+    ) -> Tuple['ComputationalBasisProductState', 'ComputationalBasisProductState']:
+        extracted = ComputationalBasisProductState([self.basis[i] for i in axes])
+        remainder = ComputationalBasisProductState(
+            [self.basis[i] for i in range(len(self.basis)) if i not in axes]
+        )
+        return extracted, remainder
+
+    def reindex(self, axes: Sequence[int]) -> 'ComputationalBasisProductState':
+        return ComputationalBasisProductState([self.basis[i] for i in axes])
+
+    @property
+    def supports_factor(self) -> bool:
+        return True
+
+
+class ComputationalBasisSimProductState(cirq.SimulationState[ComputationalBasisProductState]):
+    def __init__(self, initial_state, qubits, classical_data):
+        state = ComputationalBasisProductState(
+            cirq.big_endian_int_to_bits(initial_state, bit_count=len(qubits))
+        )
+        super().__init__(state=state, qubits=qubits, classical_data=classical_data)
+
+    def _act_on_fallback_(self, action, qubits: Sequence[cirq.Qid], allow_decompose: bool = True):
+        gate = action.gate if isinstance(action, cirq.Operation) else action
+        if isinstance(gate, cirq.XPowGate):
+            i = self.qubit_map[qubits[0]]
+            self._state.basis[i] = int(gate.exponent + self._state.basis[i]) % qubits[0].dimension
+            return True
+        pass
+
+
+def test_product_state_mode():
+    sim = CustomStateSimulator(ComputationalBasisSimProductState, split_untangled_states=True)
+    circuit = create_test_circuit()
+    r = sim.simulate(circuit)
+    assert r.measurements == {'a': np.array([1]), 'b': np.array([2])}
+
+    # Ensure the state is in product-state mode, and it's got three states (q0, q1, phase)
+    assert isinstance(r._final_simulator_state, cirq.SimulationProductState)
+    assert len(r._final_simulator_state.sim_states) == 3
+    assert r._final_simulator_state.create_merged_state()._state.basis == [2, 2]