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refactor: probabilities are now returned as an array, being aligned with Qibo

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This commit is contained in:
MatteoRobbiati
2025-02-10 10:23:01 +01:00
parent d959c95535
commit 3e9189e49f
2 changed files with 9 additions and 10 deletions
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6
src/qibotn/result.py
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@@ -31,7 +31,7 @@ class TensorNetworkResult:
probabilities = measured_probabilities[self.prob_type]
else:
probabilities = self.measured_probabilities[self.prob_type]
return probabilities
return self.backend.cast(list(probabilities.values()), dtype="double")
def frequencies(self):
"""Return frequencies if a certain number of shots has been set."""
@@ -44,10 +44,10 @@ class TensorNetworkResult:
return self.measures
def state(self):
"""Return the statevector if the number of qubits is less than 30."""
"""Return the statevector if the number of qubits is less than 20."""
if self.nqubits < 20:
return self.statevector
raise_error(
NotImplementedError,
f"Tensor network simulation cannot be used to reconstruct statevector for >= 30 .",
f"Tensor network simulation cannot be used to reconstruct statevector for >= 20 .",
)

13
tests/test_circuit_execution.py
View File

@@ -36,7 +36,6 @@ def construct_targets(nqubits):
def test_probabilities(backend, nqubits):
circ = build_GHZ(nqubits=nqubits)
ones, zeros = construct_targets(nqubits)
if isinstance(backend, QMatchaTeaBackend):
# unbiased prob
@@ -46,8 +45,8 @@ def test_probabilities(backend, nqubits):
num_samples=1000,
).probabilities()
math.isclose(out_u[ones], 0.5, abs_tol=1e-7)
math.isclose(out_u[zeros], 0.5, abs_tol=1e-7)
math.isclose(out_u[0], 0.5, abs_tol=1e-7)
math.isclose(out_u[1], 0.5, abs_tol=1e-7)
out_g = backend.execute_circuit(
circuit=circ,
@@ -55,8 +54,8 @@ def test_probabilities(backend, nqubits):
prob_threshold=1.0,
).probabilities()
math.isclose(out_g[ones], 0.5, abs_tol=1e-7)
math.isclose(out_g[zeros], 0.5, abs_tol=1e-7)
math.isclose(out_g[0], 0.5, abs_tol=1e-7)
math.isclose(out_g[1], 0.5, abs_tol=1e-7)
out_e = backend.execute_circuit(
circuit=circ,
@@ -64,8 +63,8 @@ def test_probabilities(backend, nqubits):
prob_threshold=0.2,
).probabilities()
math.isclose(out_e[ones], 0.5, abs_tol=1e-7)
math.isclose(out_e[zeros], 0.5, abs_tol=1e-7)
math.isclose(out_e[0], 0.5, abs_tol=1e-7)
math.isclose(out_e[1], 0.5, abs_tol=1e-7)
@pytest.mark.parametrize("nqubits", [2, 10, 40])