Complex question answering (CQA) requires multi-hop reasoning to combine multiple pieces of evidences ideally from different knowledge sources. Considering the insufficient labeled data in a single knowledge source and expensive human annotations, we study knowledge transfer for CQA between heterogeneous sources including a text corpus and a knowledge base (KB). To facilitate knowledge transfer between sources, we first propose a unified framework, SimultQA, to bridge KBQA and TextQA systems, which could leverage supervisions from both sources. By conducting experiments on CWQ and HotpotQA that are two popular datasets originally designed for KBQA and TextQA respectively, we explore how knowledge is transferred between sources following the pre-training and fine-tuning paradigm, and find that knowledge transfer between heterogeneous sources consistently improves the QA performance. We also conduct fine-grained analysis and hybrid evaluation experiments to further explain what knowledge has been transferred.