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.

翻译：近期研究揭示了当前量子系统的局限性，特别是近期量子设备上可用量子比特数量的严格限制。这一约束极大抑制了能够利用量子计算机的应用范围。此外，随着可用量子比特的增加，计算复杂度呈指数级增长，带来了额外挑战。因此，高效利用量子比特并缓解当前限制与未来复杂性的需求日益迫切。为解决这一问题，现有量子应用尝试在混合框架中集成经典与量子系统。本研究聚焦于量子深度学习，提出了一种名为co-TenQu的经典-量子协同架构。经典组件采用张量网络进行压缩与特征提取，使得更高维度的数据能够在有限量子比特的逻辑量子电路上编码。在量子端，我们提出了一种基于量子态保真度的评估函数，通过两侧之间的反馈循环对网络进行迭代训练。co-TenQu已在模拟器与IBM-Q平台上实现并评估。与最先进方法相比，co-TenQu在公平设置下将经典深度神经网络的性能提升高达41.72%。此外，其性能较其他量子方法提升达1.9倍，并在使用量子比特数量减少70.59%的情况下达到相近的准确率。