Hybrid Quantum-Classical Computing in Federated Learning With Data Heterogeneity
Keita Hisamori, Yi-Han Chiang, Hai Feng Lin, Yusheng Ji · 2024
Federated learning (FL) has emerged as a promising technique to realize distributed machine learning (ML) in practice. FL enables multiple clients to collaboratively train a common ML model without the need to collect raw data from clients, which therefore has merit in the protection of data privacy. On the other hand, it is known that quantum computing excels in solving specific problems that are computationally prohibitive on classical computers due to its ability to harness quantum superposition and entanglement. However, current noisy intermediate-scale quantum (NISQ) computers have difficulties in dealing with many-qubit computation due to the lack of reliable error-correction schemes, which renders hybrid quantum-classical computing with few qubits a promising alternative. In this paper, we investigate how hybrid quantum-classical computing can be applied to FL while considering the data heterogeneity among clients. To this end, we propose the two-qubit quantum circuit-embedded convolutional neural network (2QCNN) for each client, which incorporates parallel two-qubit variational quantum circuits (VQCs) between the fully connected layers of the CNN. Simulation results show that 2QCNN outperforms the comparison schemes in terms of test accuracies. In addition, the impacts of the number and depth of parallel two-qubit VQCs on the performance of 2QCNN under various degrees of data heterogeneity are further evaluated.