Cirq monofsv cleanup (quantumlib#5685)

Fixes quantumlib#3494.

This PR bundles together a bunch of cleanup around `[final_]state_vector` deprecations.

Author: 95-martin-orion

cirq-core/cirq/circuits/circuit.py

@@ -1091,12 +1091,11 @@ def _superoperator_(self) -> np.ndarray:
     )
     def final_state_vector(
         self,
-        # TODO(v0.16): Force kwargs and match order found in:
-        # cirq-core/cirq/sim/mux.py:final_state_vector
+        *,
         initial_state: 'cirq.STATE_VECTOR_LIKE' = 0,
         qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT,
         qubits_that_should_be_present: Iterable['cirq.Qid'] = (),
-        ignore_terminal_measurements: Optional[bool] = None,
+        ignore_terminal_measurements: bool = False,
         dtype: Type[np.complexfloating] = np.complex128,
         param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
         seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
@@ -1120,7 +1119,7 @@ def final_state_vector(
                 regardless when generating the matrix.
             ignore_terminal_measurements: When set, measurements at the end of
                 the circuit are ignored instead of causing the method to
-                fail.
+                fail. Defaults to False.
             dtype: The `numpy.dtype` used by the simulation. Typically one of
                 `numpy.complex64` or `numpy.complex128`.
             param_resolver: Parameters to run with the program.
@@ -1138,15 +1137,6 @@ def final_state_vector(
             ValueError: If the program doesn't have a well defined final state
                 because it has non-unitary gates.
         """
-        if ignore_terminal_measurements is None:
-            if self.has_measurements():
-                _compat._warn_or_error(
-                    '`ignore_terminal_measurements` will default to False in v0.16. '
-                    'To drop terminal measurements, please explicitly include '
-                    '`ignore_terminal_measurements=True` when calling this method.'
-                )
-            ignore_terminal_measurements = True
-
         from cirq.sim.mux import final_state_vector
 
         program = Circuit(cirq.I(q) for q in qubits_that_should_be_present) + self

cirq-core/cirq/circuits/circuit_operation_test.py

@@ -269,7 +269,7 @@ def test_recursive_params():
 
     # First example should behave like an X when simulated
     result = cirq.Simulator().simulate(cirq.Circuit(circuitop), param_resolver=outer_params)
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
 
 
 @pytest.mark.parametrize('add_measurements', [True, False])
@@ -345,9 +345,9 @@ def test_repeat_zero_times(add_measurements, use_repetition_ids, initial_reps):
         subcircuit.freeze(), repetitions=initial_reps, use_repetition_ids=use_repetition_ids
     )
     result = cirq.Simulator().simulate(cirq.Circuit(op))
-    assert np.allclose(result.state_vector(copy=False), [0, 1] if initial_reps % 2 else [1, 0])
+    assert np.allclose(result.state_vector(), [0, 1] if initial_reps % 2 else [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op**0))
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
 
 
 def test_no_repetition_ids():
@@ -377,13 +377,13 @@ def test_parameterized_repeat():
     assert cirq.parameter_names(op) == {'a'}
     assert not cirq.has_unitary(op)
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 0})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 2})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': -1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     with pytest.raises(TypeError, match='Only integer or sympy repetitions are allowed'):
         cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1.5})
     with pytest.raises(ValueError, match='Circuit contains ops whose symbols were not specified'):
@@ -392,13 +392,13 @@ def test_parameterized_repeat():
     assert cirq.parameter_names(op) == {'a'}
     assert not cirq.has_unitary(op)
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 0})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 2})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': -1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     with pytest.raises(TypeError, match='Only integer or sympy repetitions are allowed'):
         cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1.5})
     with pytest.raises(ValueError, match='Circuit contains ops whose symbols were not specified'):
@@ -407,11 +407,11 @@ def test_parameterized_repeat():
     assert cirq.parameter_names(op) == {'a', 'b'}
     assert not cirq.has_unitary(op)
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1, 'b': 1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 2, 'b': 1})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1, 'b': 2})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     with pytest.raises(TypeError, match='Only integer or sympy repetitions are allowed'):
         cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1.5, 'b': 1})
     with pytest.raises(ValueError, match='Circuit contains ops whose symbols were not specified'):
@@ -420,11 +420,11 @@ def test_parameterized_repeat():
     assert cirq.parameter_names(op) == {'a', 'b'}
     assert not cirq.has_unitary(op)
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1, 'b': 1})
-    assert np.allclose(result.state_vector(copy=False), [1, 0])
+    assert np.allclose(result.state_vector(), [1, 0])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1.5, 'b': 1})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     result = cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1, 'b': 1.5})
-    assert np.allclose(result.state_vector(copy=False), [0, 1])
+    assert np.allclose(result.state_vector(), [0, 1])
     with pytest.raises(TypeError, match='Only integer or sympy repetitions are allowed'):
         cirq.Simulator().simulate(cirq.Circuit(op), param_resolver={'a': 1.5, 'b': 1.5})
     with pytest.raises(ValueError, match='Circuit contains ops whose symbols were not specified'):

cirq-core/cirq/contrib/quantum_volume/quantum_volume.py

@@ -81,14 +81,12 @@ def compute_heavy_set(circuit: cirq.Circuit) -> List[int]:
     # output is defined in terms of probabilities, where our wave function is in
     # terms of amplitudes. We convert it by using the Born rule: squaring each
     # amplitude and taking their absolute value
-    median = np.median(np.abs(results.state_vector(copy=False) ** 2))
+    median = np.median(np.abs(results.state_vector() ** 2))
 
     # The output wave function is a vector from the result value (big-endian) to
     # the probability of that bit-string. Return all of the bit-string
     # values that have a probability greater than the median.
-    return [
-        idx for idx, amp in enumerate(results.state_vector(copy=False)) if np.abs(amp**2) > median
-    ]
+    return [idx for idx, amp in enumerate(results.state_vector()) if np.abs(amp**2) > median]
 
 
 @dataclass

cirq-core/cirq/experiments/xeb_simulation.py

@@ -65,8 +65,7 @@ def __call__(self, task: _Simulate2qXEBTask) -> List[Dict[str, Any]]:
             if cycle_depth not in cycle_depths:
                 continue
 
-            # copy=False is safe because state_vector_to_probabilities will copy anyways
-            psi = step_result.state_vector(copy=False)
+            psi = step_result.state_vector()
             pure_probs = value.state_vector_to_probabilities(psi)
 
             records += [

cirq-core/cirq/ops/boolean_hamiltonian_test.py

@@ -80,11 +80,7 @@ def test_circuit(boolean_str):
 
     circuit.append(hamiltonian_gate.on(*qubits))
 
-    phi = (
-        cirq.Simulator()
-        .simulate(circuit, qubit_order=qubits, initial_state=0)
-        .state_vector(copy=False)
-    )
+    phi = cirq.Simulator().simulate(circuit, qubit_order=qubits, initial_state=0).state_vector()
     actual = np.arctan2(phi.real, phi.imag) - math.pi / 2.0 > 0.0
 
     # Compare the two:

cirq-core/cirq/sim/mux.py

@@ -172,7 +172,7 @@ def final_state_vector(
         param_resolver=param_resolver,
     )
 
-    return result.state_vector(copy=False)
+    return result.state_vector()
 
 
 def sample_sweep(

cirq-core/cirq/sim/simulator_test.py

@@ -430,9 +430,7 @@ def _kraus_(self):
             cirq.Circuit(Reset11To00().on(*cirq.LineQubit.range(2))), initial_state=k
         )
         np.testing.assert_allclose(
-            out.state_vector(copy=False),
-            cirq.one_hot(index=k % 3, shape=4, dtype=np.complex64),
-            atol=1e-8,
+            out.state_vector(), cirq.one_hot(index=k % 3, shape=4, dtype=np.complex64), atol=1e-8
         )
 
 

cirq-core/cirq/sim/sparse_simulator.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from cirq import _compat, ops
+from cirq import ops
 from cirq.sim import simulator, state_vector, state_vector_simulator, state_vector_simulation_state
 
 if TYPE_CHECKING:
@@ -236,7 +236,7 @@ def __init__(
         self._dtype = dtype
         self._state_vector: Optional[np.ndarray] = None
 
-    def state_vector(self, copy: Optional[bool] = None):
+    def state_vector(self, copy: bool = False):
         """Return the state vector at this point in the computation.
 
         The state is returned in the computational basis with these basis
@@ -269,12 +269,6 @@ def state_vector(self, copy: Optional[bool] = None):
                 parameters from the state vector and store then using False
                 can speed up simulation by eliminating a memory copy.
         """
-        if copy is None:
-            _compat._warn_or_error(
-                "Starting in v0.16, state_vector will not copy the state by default. "
-                "Explicitly set copy=True to copy the state."
-            )
-            copy = True
         if self._state_vector is None:
             self._state_vector = np.array([1])
             state = self._merged_sim_state

cirq-core/cirq/sim/sparse_simulator_test.py

@@ -558,17 +558,6 @@ def test_simulate_moment_steps(dtype: Type[np.complexfloating], split: bool):
             np.testing.assert_almost_equal(step.state_vector(copy=True), np.array([1, 0, 0, 0]))
 
 
-def test_simulate_moment_steps_implicit_copy_deprecated():
-    q0 = cirq.LineQubit(0)
-    simulator = cirq.Simulator()
-    steps = list(simulator.simulate_moment_steps(cirq.Circuit(cirq.X(q0))))
-
-    with cirq.testing.assert_deprecated(
-        "state_vector will not copy the state by default", deadline="v0.16"
-    ):
-        _ = steps[0].state_vector()
-
-
 @pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
 @pytest.mark.parametrize('split', [True, False])
 def test_simulate_moment_steps_empty_circuit(dtype: Type[np.complexfloating], split: bool):
@@ -726,8 +715,8 @@ def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs):
 
     initial_state = np.array([np.sqrt(0.5), np.sqrt(0.5)], dtype=np.complex64)
     result = simulator.simulate(circuit, initial_state=initial_state)
-    np.testing.assert_array_almost_equal(result.state_vector(copy=False), initial_state)
-    assert not initial_state is result.state_vector(copy=False)
+    np.testing.assert_array_almost_equal(result.state_vector(), initial_state)
+    assert not initial_state is result.state_vector()
 
 
 def test_does_not_modify_initial_state():
@@ -751,7 +740,7 @@ def _apply_unitary_(self, args: cirq.ApplyUnitaryArgs):
     result = simulator.simulate(circuit, initial_state=initial_state)
     np.testing.assert_array_almost_equal(np.array([1, 0], dtype=np.complex64), initial_state)
     np.testing.assert_array_almost_equal(
-        result.state_vector(copy=False), np.array([0, 1], dtype=np.complex64)
+        result.state_vector(), np.array([0, 1], dtype=np.complex64)
     )
 
 
@@ -803,7 +792,7 @@ def test_simulates_composite():
     np.testing.assert_allclose(
         c.final_state_vector(ignore_terminal_measurements=False, dtype=np.complex64), expected
     )
-    np.testing.assert_allclose(cirq.Simulator().simulate(c).state_vector(copy=False), expected)
+    np.testing.assert_allclose(cirq.Simulator().simulate(c).state_vector(), expected)
 
 
 def test_simulate_measurement_inversions():
@@ -820,15 +809,15 @@ def test_works_on_pauli_string_phasor():
     a, b = cirq.LineQubit.range(2)
     c = cirq.Circuit(np.exp(0.5j * np.pi * cirq.X(a) * cirq.X(b)))
     sim = cirq.Simulator()
-    result = sim.simulate(c).state_vector(copy=False)
+    result = sim.simulate(c).state_vector()
     np.testing.assert_allclose(result.reshape(4), np.array([0, 0, 0, 1j]), atol=1e-8)
 
 
 def test_works_on_pauli_string():
     a, b = cirq.LineQubit.range(2)
     c = cirq.Circuit(cirq.X(a) * cirq.X(b))
     sim = cirq.Simulator()
-    result = sim.simulate(c).state_vector(copy=False)
+    result = sim.simulate(c).state_vector()
     np.testing.assert_allclose(result.reshape(4), np.array([0, 0, 0, 1]), atol=1e-8)
 
 
@@ -1325,7 +1314,7 @@ def _apply_unitary_(self, args):
     vectors = []
     copy_of_vectors = []
     for step in sim.simulate_moment_steps(circuit):
-        state_vector = step.state_vector(copy=False)
+        state_vector = step.state_vector()
         vectors.append(state_vector)
         copy_of_vectors.append(state_vector.copy())
     assert any(not np.array_equal(x, y) for x, y in zip(vectors, copy_of_vectors))
@@ -1338,9 +1327,9 @@ def test_final_state_vector_is_not_last_object():
     initial_state = np.array([1, 0], dtype=np.complex64)
     circuit = cirq.Circuit(cirq.wait(q))
     result = sim.simulate(circuit, initial_state=initial_state)
-    assert result.state_vector(copy=False) is not initial_state
-    assert not np.shares_memory(result.state_vector(copy=False), initial_state)
-    np.testing.assert_equal(result.state_vector(copy=False), initial_state)
+    assert result.state_vector() is not initial_state
+    assert not np.shares_memory(result.state_vector(), initial_state)
+    np.testing.assert_equal(result.state_vector(), initial_state)
 
 
 def test_deterministic_gate_noise():

cirq-core/cirq/sim/state_vector.py

@@ -57,7 +57,7 @@ def _qid_shape_(self) -> Tuple[int, ...]:
         return self._qid_shape
 
     @abc.abstractmethod
-    def state_vector(self, copy: Optional[bool] = False) -> np.ndarray:
+    def state_vector(self, copy: bool = False) -> np.ndarray:
         """Return the state vector (wave function).
 
         The vector is returned in the computational basis with these basis
@@ -101,9 +101,7 @@ def dirac_notation(self, decimals: int = 2) -> str:
         Returns:
             A pretty string consisting of a sum of computational basis kets
             and non-zero floats of the specified accuracy."""
-        return qis.dirac_notation(
-            self.state_vector(copy=False), decimals, qid_shape=self._qid_shape
-        )
+        return qis.dirac_notation(self.state_vector(), decimals, qid_shape=self._qid_shape)
 
     def density_matrix_of(self, qubits: List['cirq.Qid'] = None) -> np.ndarray:
         r"""Returns the density matrix of the state.
@@ -139,7 +137,7 @@ def density_matrix_of(self, qubits: List['cirq.Qid'] = None) -> np.ndarray:
                 corresponding to the state.
         """
         return qis.density_matrix_from_state_vector(
-            self.state_vector(copy=False),
+            self.state_vector(),
             [self.qubit_map[q] for q in qubits] if qubits is not None else None,
             qid_shape=self._qid_shape,
         )
@@ -164,7 +162,7 @@ def bloch_vector_of(self, qubit: 'cirq.Qid') -> np.ndarray:
                 corresponding to the state.
         """
         return qis.bloch_vector_from_state_vector(
-            self.state_vector(copy=False), self.qubit_map[qubit], qid_shape=self._qid_shape
+            self.state_vector(), self.qubit_map[qubit], qid_shape=self._qid_shape
         )
 
 

cirq-core/cirq/sim/state_vector_simulator.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from cirq import _compat, ops, value, qis
+from cirq import ops, value, qis
 from cirq._compat import proper_repr
 from cirq.sim import simulator, state_vector, simulator_base
 
@@ -130,7 +130,7 @@ def final_state_vector(self) -> np.ndarray:
             self._final_state_vector = tensor
         return self._final_state_vector
 
-    def state_vector(self, copy: bool = None) -> np.ndarray:
+    def state_vector(self, copy: bool = False) -> np.ndarray:
         """Return the state vector at the end of the computation.
 
         The state is returned in the computational basis with these basis
@@ -155,13 +155,14 @@ def state_vector(self, copy: bool = None) -> np.ndarray:
                 |  5  |   1    |   0    |   1    |
                 |  6  |   1    |   1    |   0    |
                 |  7  |   1    |   1    |   1    |
+
+        Args:
+            copy: If True, the returned state vector will be a copy of that
+            stored by the object. This is potentially expensive for large
+            state vectors, but prevents mutation of the object state, e.g. for
+            operating on intermediate states of a circuit.
+            Defaults to False.
         """
-        if copy is None:
-            _compat._warn_or_error(
-                "Starting in v0.16, state_vector will not copy the state by default. "
-                "Explicitly set copy=True to copy the state."
-            )
-            copy = True
         return self.final_state_vector.copy() if copy else self.final_state_vector
 
     def _value_equality_values_(self):