VectorStoreIndex over QdrantVectorStore

Write a Python function create_qdrant_index that wires a VectorStoreIndex over a QdrantVectorStore following the same pattern as build_index_from_nodes in the repository source. The function receives:

• client: an already-initialized QdrantClient instance.
• collection_name: a string naming the Qdrant collection.
• nodes: a list of TextNode objects whose .embedding attribute is already set (pre-embedded).
• embed_model: an embedding model (e.g., OpenAIEmbedding instance).

Your implementation must:

1. Create a QdrantVectorStore with index_doc_id=True and text_key="text" so that Qdrant becomes the single source of truth (node text and metadata live in the point payload).
2. Build a StorageContext from defaults that uses this vector store.
3. Build and return a VectorStoreIndex from the pre-embedded nodes using that storage context and the given embed model – LlamaIndex must never re-embed.

Write the function body only (no class or extra code). Assume all necessary imports are already available (e.g., from llama_index.vector_stores.qdrant import QdrantVectorStore, from llama_index.core import StorageContext, VectorStoreIndex).