Remove Tensor and Hamiltonian in python frontend for lightning (#994)

We are removing the Tensor and Hamiltonian classes, so we should remove
their (minimal) remaining references in the Lightning python code.

Blocks running CI for the [PL PR
legacy_opmath, since every test that uses `lightning.qubit` fails with
Tensor removed.

---------

Co-authored-by: ringo-but-quantum <[email protected]>

Author: 2 people

.github/CHANGELOG.md

@@ -13,6 +13,9 @@
 
 ### Breaking changes
 
+* Handling for the legacy operator arithmetic (the `Hamiltonian` and `Tensor` classes in PennyLane) is removed.
+  [(#994)](https://github.com/PennyLaneAI/pennylane-lightning/pull/994)
+
 ### Improvements
 
 * Unify excitation gates memory layout to row-major for both LGPU and LT.

pennylane_lightning/core/_measurements_base.py

@@ -32,7 +32,7 @@
     StateMeasurement,
     VarianceMP,
 )
-from pennylane.ops import Hamiltonian, SparseHamiltonian, Sum
+from pennylane.ops import SparseHamiltonian, Sum
 from pennylane.tape import QuantumScript
 from pennylane.typing import Result, TensorLike
 from pennylane.wires import Wires
@@ -117,7 +117,7 @@ def expval(self, measurementprocess: MeasurementProcess):
             )
 
         if (
-            isinstance(measurementprocess.obs, (qml.ops.Hamiltonian, qml.Hermitian))
+            isinstance(measurementprocess.obs, qml.Hermitian)
             or (measurementprocess.obs.arithmetic_depth > 0)
             or isinstance(measurementprocess.obs.name, List)
         ):
@@ -176,7 +176,7 @@ def var(self, measurementprocess: MeasurementProcess):
             )
 
         if (
-            isinstance(measurementprocess.obs, (qml.ops.Hamiltonian, qml.Hermitian))
+            isinstance(measurementprocess.obs, qml.Hermitian)
             or (measurementprocess.obs.arithmetic_depth > 0)
             or isinstance(measurementprocess.obs.name, List)
         ):
@@ -307,15 +307,13 @@ def measure_with_samples(
                 raise TypeError(
                     "ExpectationMP/VarianceMP(SparseHamiltonian) cannot be computed with samples."
                 )
-            if isinstance(group[0], VarianceMP) and isinstance(group[0].obs, (Hamiltonian, Sum)):
-                raise TypeError("VarianceMP(Hamiltonian/Sum) cannot be computed with samples.")
+            if isinstance(group[0], VarianceMP) and isinstance(group[0].obs, Sum):
+                raise TypeError("VarianceMP(Sum) cannot be computed with samples.")
             if isinstance(group[0], (ClassicalShadowMP, ShadowExpvalMP)):
                 raise TypeError(
                     "ExpectationMP(ClassicalShadowMP, ShadowExpvalMP) cannot be computed with samples."
                 )
-            if isinstance(group[0], ExpectationMP) and isinstance(group[0].obs, Hamiltonian):
-                all_res.extend(self._measure_hamiltonian_with_samples(group, shots))
-            elif isinstance(group[0], ExpectationMP) and isinstance(group[0].obs, Sum):
+            if isinstance(group[0], ExpectationMP) and isinstance(group[0].obs, Sum):
                 all_res.extend(self._measure_sum_with_samples(group, shots))
             else:
                 all_res.extend(self._measure_with_samples_diagonalizing_gates(group, shots))
@@ -372,26 +370,6 @@ def _measure_with_samples_diagonalizing_gates(
             TensorLike[Any]: Sample measurement results
         """
 
-    def _measure_hamiltonian_with_samples(
-        self,
-        mp: List[SampleMeasurement],
-        shots: Shots,
-    ):
-        # the list contains only one element based on how we group measurements
-        mp = mp[0]
-
-        # if the measurement process involves a Hamiltonian, measure each
-        # of the terms separately and sum
-        def _sum_for_single_shot(s):
-            results = self.measure_with_samples(
-                [ExpectationMP(t) for t in mp.obs.terms()[1]],
-                s,
-            )
-            return sum(c * res for c, res in zip(mp.obs.terms()[0], results))
-
-        unsqueezed_results = tuple(_sum_for_single_shot(type(shots)(s)) for s in shots)
-        return [unsqueezed_results] if shots.has_partitioned_shots else [unsqueezed_results[0]]
-
     def _measure_sum_with_samples(
         self,
         mp: List[SampleMeasurement],

pennylane_lightning/core/_serialize.py

@@ -33,8 +33,7 @@
     matrix,
 )
 from pennylane.math import unwrap
-from pennylane.operation import Tensor
-from pennylane.ops import Hamiltonian, LinearCombination, Prod, SProd, Sum
+from pennylane.ops import LinearCombination, Prod, SProd, Sum
 from pennylane.tape import QuantumTape
 
 NAMED_OBS = (Identity, PauliX, PauliY, PauliZ, Hadamard)
@@ -211,8 +210,7 @@ def _hermitian_ob(self, observable, wires_map: dict = None):
 
     def _tensor_ob(self, observable, wires_map: dict = None):
         """Serialize a tensor observable"""
-        obs = observable.obs if isinstance(observable, Tensor) else observable.operands
-        return self.tensor_obs([self._ob(o, wires_map) for o in obs])
+        return self.tensor_obs([self._ob(o, wires_map) for o in observable.operands])
 
     def _chunk_ham_terms(self, coeffs, ops, split_num: int = 1) -> List:
         "Create split_num sub-Hamiltonians from a single high term-count Hamiltonian"
@@ -262,7 +260,7 @@ def _sparse_hamiltonian(self, observable, wires_map: dict = None):
         """
 
         if self._use_mpi:
-            Hmat = Hamiltonian([1.0], [Identity(0)]).sparse_matrix()
+            Hmat = Identity(0).sparse_matrix()
             H_sparse = SparseHamiltonian(Hmat, wires=range(1))
             spm = H_sparse.sparse_matrix()
             # Only root 0 needs the overall sparse matrix data
@@ -323,11 +321,9 @@ def _ob(self, observable, wires_map: dict = None):
         """Serialize a :class:`pennylane.operation.Observable` into an Observable."""
         if isinstance(observable, NAMED_OBS):
             return self._named_obs(observable, wires_map)
-        if isinstance(observable, Hamiltonian):
-            return self._hamiltonian(observable, wires_map)
         if observable.pauli_rep is not None:
             return self._pauli_sentence(observable.pauli_rep, wires_map)
-        if isinstance(observable, (Tensor, Prod)):
+        if isinstance(observable, Prod):
             if isinstance(observable, Prod) and observable.has_overlapping_wires:
                 return self._hermitian_ob(observable, wires_map)
             return self._tensor_ob(observable, wires_map)

pennylane_lightning/core/_version.py

@@ -16,4 +16,5 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.40.0-dev9"
+
+__version__ = "0.40.0-dev8"

pennylane_lightning/core/lightning_base.py

@@ -24,7 +24,7 @@
 from pennylane import BasisState, StatePrep
 from pennylane.devices import QubitDevice
 from pennylane.measurements import Expectation, MeasurementProcess, State
-from pennylane.operation import Operation, Tensor
+from pennylane.operation import Operation
 from pennylane.ops import Prod, Projector, SProd, Sum
 from pennylane.wires import Wires
 
@@ -178,9 +178,7 @@ def probability(self, wires=None, shot_range=None, bin_size=None):
     def _get_diagonalizing_gates(self, circuit: qml.tape.QuantumTape) -> List[Operation]:
         # pylint: disable=no-member, protected-access
         def skip_diagonalizing(obs):
-            return isinstance(obs, qml.Hamiltonian) or (
-                isinstance(obs, qml.ops.Sum) and obs._pauli_rep is not None
-            )
+            return isinstance(obs, qml.ops.Sum) and obs._pauli_rep is not None
 
         meas_filtered = list(
             filter(lambda m: m.obs is None or not skip_diagonalizing(m.obs), circuit.measurements)
@@ -319,18 +317,12 @@ def _assert_adjdiff_no_projectors(observable):
         Raises:
             ~pennylane.QuantumFunctionError: if a ``Projector`` is found.
         """
-        if isinstance(observable, Tensor):
-            if any(isinstance(o, Projector) for o in observable.non_identity_obs):
-                raise qml.QuantumFunctionError(
-                    "Adjoint differentiation method does not support the Projector observable"
-                )
-
-        elif isinstance(observable, Projector):
+        if isinstance(observable, Projector):
             raise qml.QuantumFunctionError(
                 "Adjoint differentiation method does not support the Projector observable"
             )
 
-        elif isinstance(observable, SProd):
+        if isinstance(observable, SProd):
             LightningBase._assert_adjdiff_no_projectors(observable.base)
 
         elif isinstance(observable, (Sum, Prod)):

pennylane_lightning/lightning_gpu/lightning_gpu.py

@@ -42,7 +42,7 @@
     validate_observables,
 )
 from pennylane.measurements import MidMeasureMP
-from pennylane.operation import DecompositionUndefinedError, Operator, Tensor
+from pennylane.operation import DecompositionUndefinedError, Operator
 from pennylane.ops import Prod, SProd, Sum
 from pennylane.tape import QuantumScript
 from pennylane.transforms.core import TransformProgram
@@ -169,7 +169,6 @@
         "PauliZ",
         "Hadamard",
         "SparseHamiltonian",
-        "Hamiltonian",
         "LinearCombination",
         "Hermitian",
         "Identity",
@@ -204,11 +203,6 @@ def adjoint_observables(obs: Operator) -> bool:
     if isinstance(obs, qml.Projector):
         return False
 
-    if isinstance(obs, Tensor):
-        if any(isinstance(o, qml.Projector) for o in obs.non_identity_obs):
-            return False
-        return True
-
     if isinstance(obs, SProd):
         return adjoint_observables(obs.base)
 

pennylane_lightning/lightning_kokkos/lightning_kokkos.py

@@ -38,7 +38,7 @@
     validate_observables,
 )
 from pennylane.measurements import MidMeasureMP
-from pennylane.operation import DecompositionUndefinedError, Operator, Tensor
+from pennylane.operation import DecompositionUndefinedError, Operator
 from pennylane.ops import Prod, SProd, Sum
 from pennylane.tape import QuantumScript
 from pennylane.transforms.core import TransformProgram
@@ -138,7 +138,6 @@
         "Identity",
         "Projector",
         "SparseHamiltonian",
-        "Hamiltonian",
         "LinearCombination",
         "Sum",
         "SProd",
@@ -174,11 +173,6 @@ def adjoint_observables(obs: Operator) -> bool:
     if isinstance(obs, qml.Projector):
         return False
 
-    if isinstance(obs, Tensor):
-        if any(isinstance(o, qml.Projector) for o in obs.non_identity_obs):
-            return False
-        return True
-
     if isinstance(obs, SProd):
         return adjoint_observables(obs.base)
 

pennylane_lightning/lightning_qubit/lightning_qubit.py

@@ -38,7 +38,7 @@
     validate_observables,
 )
 from pennylane.measurements import MidMeasureMP
-from pennylane.operation import DecompositionUndefinedError, Operator, Tensor
+from pennylane.operation import DecompositionUndefinedError, Operator
 from pennylane.ops import Prod, SProd, Sum
 from pennylane.tape import QuantumScript
 from pennylane.transforms.core import TransformProgram
@@ -159,7 +159,6 @@
         "Identity",
         "Projector",
         "SparseHamiltonian",
-        "Hamiltonian",
         "LinearCombination",
         "Sum",
         "SProd",
@@ -200,11 +199,6 @@ def adjoint_observables(obs: Operator) -> bool:
     if isinstance(obs, qml.Projector):
         return False
 
-    if isinstance(obs, Tensor):
-        if any(isinstance(o, qml.Projector) for o in obs.non_identity_obs):
-            return False
-        return True
-
     if isinstance(obs, SProd):
         return adjoint_observables(obs.base)
 

pennylane_lightning/lightning_tensor/lightning_tensor.py

@@ -154,7 +154,6 @@
         "Hadamard",
         "Hermitian",
         "Identity",
-        "Hamiltonian",
         "LinearCombination",
         "Sum",
         "SProd",

tests/conftest.py

@@ -206,34 +206,6 @@ def _statevector(num_wires):
     return _statevector
 
 
-#######################################################################
-# Fixtures for testing under new and old opmath
-
-
-@pytest.fixture(scope="function")
-def use_legacy_opmath():
-    with qml.operation.disable_new_opmath_cm() as cm:
-        yield cm
-
-
-@pytest.fixture(scope="function")
-def use_new_opmath():
-    with qml.operation.enable_new_opmath_cm() as cm:
-        yield cm
-
-
-@pytest.fixture(
-    params=[qml.operation.disable_new_opmath_cm, qml.operation.enable_new_opmath_cm],
-    scope="function",
-)
-def use_legacy_and_new_opmath(request):
-    with request.param() as cm:
-        yield cm
-
-
-#######################################################################
-
-
 def validate_counts(shots, results1, results2):
     """Compares two counts.
 

tests/lightning_qubit/test_adjoint_jacobian_class.py

@@ -281,7 +281,6 @@ def test_multiple_rx_gradient_expval_hermitian(self, tol, lightning_sv):
 
         assert np.allclose(expected, result, atol=tol, rtol=0)
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     def test_multiple_rx_gradient_expval_hamiltonian(self, tol, lightning_sv):
         """Tests that the gradient of multiple RX gates in a circuit yields the correct result
         with Hermitian observable
@@ -392,7 +391,6 @@ def calculate_vjp(statevector, tape, vector):
 
         return LightningAdjointJacobian(statevector).calculate_vjp(tape, vector)
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     def test_multiple_measurements(self, tol, lightning_sv):
         """Tests provides correct answer when provided multiple measurements."""
         x, y, z = [0.5, 0.3, -0.7]

tests/lightning_qubit/test_jacobian_method.py

@@ -139,7 +139,6 @@ def process_and_execute(statevector, tape, dy, execute_and_derivatives=False):
             jac = device.vjp(tape, dy, statevector)
         return results, jac
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
     @pytest.mark.parametrize(
         "obs",

tests/new_api/test_device.py

@@ -132,12 +132,12 @@ def test_accepted_observables(self):
     @pytest.mark.parametrize(
         "obs, expected",
         [
-            (qml.operation.Tensor(qml.Projector([0], 0), qml.PauliZ(1)), False),
+            (qml.prod(qml.Projector([0], 0), qml.PauliZ(1)), False),
             (qml.prod(qml.Projector([0], 0), qml.PauliZ(1)), False),
             (qml.s_prod(1.5, qml.Projector([0], 0)), False),
             (qml.sum(qml.Projector([0], 0), qml.Hadamard(1)), False),
             (qml.sum(qml.prod(qml.Projector([0], 0), qml.Y(1)), qml.PauliX(1)), False),
-            (qml.operation.Tensor(qml.Y(0), qml.Z(1)), True),
+            (qml.prod(qml.Y(0), qml.Z(1)), True),
             (qml.prod(qml.Y(0), qml.PauliZ(1)), True),
             (qml.s_prod(1.5, qml.Y(1)), True),
             (qml.sum(qml.Y(1), qml.Hadamard(1)), True),
@@ -429,7 +429,6 @@ def test_preprocess_state_prep_middle_op_decomposition(self, op, decomp_depth):
         )
         assert qml.equal(new_tape, expected_tape)
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
     @pytest.mark.parametrize(
         "mp",
@@ -443,7 +442,7 @@ def test_preprocess_state_prep_middle_op_decomposition(self, op, decomp_depth):
                 qml.expval(qml.Hamiltonian([-0.5, 1.5], [qml.Y(1), qml.X(1)])),
                 qml.expval(2.5 * qml.Z(0)),
                 qml.expval(qml.Z(0) @ qml.X(1)),
-                qml.expval(qml.operation.Tensor(qml.Z(0), qml.X(1))),
+                qml.expval(qml.prod(qml.Z(0), qml.X(1))),
                 qml.expval(
                     qml.SparseHamiltonian(
                         qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]).sparse_matrix(
@@ -483,7 +482,6 @@ def test_execute_single_measurement(self, theta, phi, mp, dev):
         expected = self.calculate_reference(qs)[0]
         assert np.allclose(res, expected)
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
     @pytest.mark.parametrize(
         "mp1",
@@ -652,7 +650,6 @@ def test_supports_derivatives(self, dev, config, tape, expected, batch_obs):
         """Test that supports_derivative returns the correct boolean value."""
         assert dev.supports_derivatives(config, tape) == expected
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
     @pytest.mark.parametrize(
         "obs",
@@ -1038,7 +1035,6 @@ def test_supports_vjp(self, dev, config, tape, expected, batch_obs):
         """Test that supports_vjp returns the correct boolean value."""
         assert dev.supports_vjp(config, tape) == expected
 
-    @pytest.mark.usefixtures("use_legacy_and_new_opmath")
     @pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
     @pytest.mark.parametrize(
         "obs",