tests

tests/test_quantum_info_entropies.py

@@ -2,7 +2,12 @@
 import pytest
 
 from qibo.config import PRECISION_TOL
-from qibo.quantum_info.entropies import entanglement_entropy, entropy, shannon_entropy
+from qibo.quantum_info.entropies import (
+    classical_relative_entropy,
+    entanglement_entropy,
+    entropy,
+    shannon_entropy,
+)
 from qibo.quantum_info.random_ensembles import random_statevector, random_unitary
 
 
@@ -168,3 +173,63 @@ def test_entanglement_entropy(backend, bipartition, base, check_hermitian):
     )
 
     backend.assert_allclose(entang_entrop, 0.0, atol=PRECISION_TOL)
+
+
+@pytest.mark.parametrize("kind", [None, list])
+@pytest.mark.parametrize("validate", [False, True])
+@pytest.mark.parametrize("base", [2, 10, np.e, 5])
+def test_classical_relative_entropy(backend, base, validate, kind):
+    with pytest.raises(TypeError):
+        prob = np.random.rand(1, 2)
+        prob_q = np.random.rand(1, 5)
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, backend=backend)
+    with pytest.raises(TypeError):
+        prob = np.random.rand(1, 2)[0]
+        prob_q = np.array([])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([-1, 2.0])
+        prob_q = np.random.rand(1, 5)[0]
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, validate=True, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.random.rand(1, 2)[0]
+        prob_q = np.array([1.0, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, validate=True, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.0, 0.0])
+        prob_q = np.random.rand(1, 2)[0]
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, validate=True, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.0, 0.0])
+        prob_q = np.array([0.0, 1.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        prob_q = backend.cast(prob_q, dtype=prob_q.dtype)
+        test = classical_relative_entropy(prob, prob_q, base=-2, backend=backend)
+
+    prob_p = np.random.rand(10)
+    prob_q = np.random.rand(10)
+    prob_p /= np.sum(prob_p)
+    prob_q /= np.sum(prob_q)
+
+    target = np.sum(prob_p * np.log(prob_p) / np.log(base)) - np.sum(
+        prob_p * np.log(prob_q) / np.log(base)
+    )
+
+    if kind is not None:
+        prob_p, prob_q = kind(prob_p), kind(prob_q)
+
+    divergence = classical_relative_entropy(
+        prob_p, prob_q, base=base, validate=validate, backend=backend
+    )
+
+    backend.assert_allclose(divergence, target, atol=1e-5)