feat: test for observable default value

src/qiboml/models/decoding.py

@@ -74,7 +74,7 @@ class Expectation(QuantumDecoding):
 
     def __post_init__(self):
         if self.observable is None:
-            self.observable = Z(self.nqubits, dense=True, backend=self.backend)
+            self.observable = Z(self.nqubits, dense=False, backend=self.backend)
         super().__post_init__()
 
     def __call__(self, x: Circuit) -> ndarray:

tests/#test_models_decoding.py#

@@ -0,0 +1,56 @@
+import numpy as np
+import pytest
+from qibo import gates, hamiltonians
+from qibo.quantum_info import random_clifford
+from qibo.symbols import Z
+
+import qiboml.models.decoding as dec
+
+
+def test_probabilities_layer(backend):
+    nqubits = 5
+    qubits = np.random.choice(range(nqubits), size=(4,), replace=False)
+    layer = dec.Probabilities(nqubits, qubits=qubits, backend=backend)
+    c = random_clifford(nqubits, backend=backend)
+    backend.assert_allclose(
+        layer(c).ravel(), backend.execute_circuit(c).probabilities()
+    )
+
+
+def test_state_layer(backend):
+    nqubits = 5
+    layer = dec.State(nqubits, backend=backend)
+    c = random_clifford(nqubits, backend=backend)
+    real, im = layer(c)
+    backend.assert_allclose(
+        (real + 1j * im).ravel(), backend.execute_circuit(c).state().ravel()
+    )
+
+
+@pytest.mark.parametrize("nshots", [None, 10000])
+@pytest.mark.parametrize("observable", [None, lambda n,b: hamiltonians.SymbolicHamiltonian(sum([Z(i) for i in range(n)]), nqubits=n, backend=b)])
+def test_expectation_layer(backend, nshots, observable):
+    backend.set_seed(42)
+    rng = np.random.default_rng(42)
+    nqubits = 5
+
+    c = random_clifford(nqubits, seed=rng, backend=backend)
+    if observable is not None:
+        observable = observable(nqubits, backend)
+    layer = dec.Expectation(
+        nqubits,
+        observable=observable,
+        nshots=nshots,
+        backend=backend,
+    )
+    layer_expv = layer(c)
+    if observable is None:
+        observable = hamiltonians.Z(nqubits, dense=False, backend=backend)
+    expv = (
+        observable.expectation(backend.execute_circuit(c).state())
+        if nshots is None
+        else observable.expectation_from_samples(
+            layer.circuit.measurements[0].result.frequencies()
+        )
+    )
+    backend.assert_allclose(layer_expv, expv)

tests/.#test_models_decoding.py

@@ -0,0 +1 @@
+andrea@ubuntu-desktop.9199:1738047566