diff --git a/integrations/qdrant/tests/conftest.py b/integrations/qdrant/tests/conftest.py
new file mode 100644
index 000000000..4bdf0bad2
--- /dev/null
+++ b/integrations/qdrant/tests/conftest.py
@@ -0,0 +1,18 @@
+import numpy as np
+import pytest
+from haystack.dataclasses import SparseEmbedding
+
+
+@pytest.fixture
+def generate_sparse_embedding():
+    """
+    This fixture generates a random SparseEmbedding object each time it is used.
+    """
+
+    def _generate_random_sparse_embedding():
+        random_indice_length = np.random.randint(3, 15)
+        indices = list(range(random_indice_length))
+        values = [np.random.random_sample() for _ in range(random_indice_length)]
+        return SparseEmbedding(indices=indices, values=values)
+
+    return _generate_random_sparse_embedding
diff --git a/integrations/qdrant/tests/test_document_store.py b/integrations/qdrant/tests/test_document_store.py
index acc09b11f..bc42a11a5 100644
--- a/integrations/qdrant/tests/test_document_store.py
+++ b/integrations/qdrant/tests/test_document_store.py
@@ -1,6 +1,5 @@
 from typing import List
 
-import numpy as np
 import pytest
 from haystack import Document
 from haystack.dataclasses import SparseEmbedding
@@ -15,18 +14,6 @@
 from haystack_integrations.document_stores.qdrant.document_store import QdrantDocumentStore, QdrantStoreError
 
 
-def _generate_mocked_sparse_embedding(n):
-    list_of_sparse_vectors = []
-    for _ in range(n):
-        random_indice_length = np.random.randint(3, 15)
-        data = {
-            "indices": list(range(random_indice_length)),
-            "values": [np.random.random_sample() for _ in range(random_indice_length)],
-        }
-        list_of_sparse_vectors.append(data)
-    return list_of_sparse_vectors
-
-
 class TestQdrantDocumentStore(CountDocumentsTest, WriteDocumentsTest, DeleteDocumentsTest):
     @pytest.fixture
     def document_store(self) -> QdrantDocumentStore:
@@ -56,14 +43,15 @@ def test_write_documents(self, document_store: QdrantDocumentStore):
         with pytest.raises(DuplicateDocumentError):
             document_store.write_documents(docs, DuplicatePolicy.FAIL)
 
-    def test_query_hybrid(self):
+    def test_query_hybrid(self, generate_sparse_embedding):
         document_store = QdrantDocumentStore(location=":memory:", use_sparse_embeddings=True)
 
         docs = []
         for i in range(20):
-            sparse_embedding = SparseEmbedding.from_dict(_generate_mocked_sparse_embedding(1)[0])
             docs.append(
-                Document(content=f"doc {i}", sparse_embedding=sparse_embedding, embedding=_random_embeddings(768))
+                Document(
+                    content=f"doc {i}", sparse_embedding=generate_sparse_embedding(), embedding=_random_embeddings(768)
+                )
             )
 
         document_store.write_documents(docs)
diff --git a/integrations/qdrant/tests/test_retriever.py b/integrations/qdrant/tests/test_retriever.py
index a2aa2b090..47fec5968 100644
--- a/integrations/qdrant/tests/test_retriever.py
+++ b/integrations/qdrant/tests/test_retriever.py
@@ -13,8 +13,6 @@
 )
 from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
 
-from .test_document_store import _generate_mocked_sparse_embedding
-
 
 class TestQdrantRetriever(FilterableDocsFixtureMixin):
     def test_init_default(self):
@@ -225,12 +223,12 @@ def test_from_dict(self):
         assert retriever._scale_score is False
         assert retriever._return_embedding is True
 
-    def test_run(self, filterable_docs: List[Document]):
+    def test_run(self, filterable_docs: List[Document], generate_sparse_embedding):
         document_store = QdrantDocumentStore(location=":memory:", index="Boi", use_sparse_embeddings=True)
 
         # Add fake sparse embedding to documents
         for doc in filterable_docs:
-            doc.sparse_embedding = SparseEmbedding.from_dict(_generate_mocked_sparse_embedding(1)[0])
+            doc.sparse_embedding = generate_sparse_embedding()
 
         document_store.write_documents(filterable_docs)
         retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)