  Recent advancements have highlighted the limitations of current quantum
systems, particularly the restricted number of qubits available on near-term
quantum devices. This constraint greatly inhibits the range of applications
that can leverage quantum computers. Moreover, as the available qubits
increase, the computational complexity grows exponentially, posing additional
challenges. Consequently, there is an urgent need to use qubits efficiently and
mitigate both present limitations and future complexities. To address this,
existing quantum applications attempt to integrate classical and quantum
systems in a hybrid framework. In this study, we concentrate on quantum deep
learning and introduce a collaborative classical-quantum architecture called
co-TenQu. The classical component employs a tensor network for compression and
feature extraction, enabling higher-dimensional data to be encoded onto logical
quantum circuits with limited qubits. On the quantum side, we propose a
quantum-state-fidelity-based evaluation function to iteratively train the
network through a feedback loop between the two sides. co-TenQu has been
implemented and evaluated with both simulators and the IBM-Q platform. Compared
to state-of-the-art approaches, co-TenQu enhances a classical deep neural
network by up to 41.72% in a fair setting. Additionally, it outperforms other
quantum-based methods by up to 1.9 times and achieves similar accuracy while
utilizing 70.59% fewer qubits.
