A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
This addresses the challenge of efficiently using qubits in quantum computing for machine learning applications, representing an incremental improvement over existing hybrid quantum-classical methods.
The paper tackles the problem of limited qubits in near-term quantum devices for quantum deep learning by introducing a collaborative classical-quantum architecture called co-TenQu, which enhances a classical deep neural network by up to 41.72%, outperforms other quantum-based methods by up to 1.9 times, and uses 70.59% fewer qubits.
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.