Distributed Quantum Neural Networks via Partitioned Features Encoding

Quantum neural networks are expected to be a promising application in near-term quantum computation, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To mitigate these challenges, distributed quantum neural networks have been proposed to make a prediction by approximating a large circuit with multiple small circuits. However, the approximation of a large circuit requires an exponential number of small circuit evaluations. Here, we instead propose to distribute partitioned features over multiple small quantum neural networks and use the ensemble of their expectation values to generate predictions. To verify our distributed approach, we demonstrate multi-class classifications of handwritten digit datasets. Especially for the MNIST dataset, we succeeded in ten class classifications of the dataset with exceeding 96% accuracy. Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network compared to a single quantum neural network. Our results highlight distributed quantum neural networks as a promising direction for practical quantum machine learning algorithms compatible with near-term quantum devices. We hope that our approach is useful for exploring quantum machine learning applications.