Towards Efficient Hybrid Quantum-Classical Learning Models

With the limitations of current noisy intermediate scale quantum (NISQ) devices, there is a need for hybrid quantum-classical learning models to operate more efficiently within existing hardware constraints. The design of cost-effective hybrid quantum-classical learning systems is therefore imperative for various applications, including biosignal processing in the healthcare sector. This motivates the investigation into the reduction of computational complexity of a hybrid quantumclassical neural network. We propose a new hybrid model combining a quantum neural network (QNN) with a binary neural network (BNN), known as quBNN, with the aim to reduce model complexity. For benchmarking, we consider a classical multilayer perceptron (MLP) and a standard nonbinarised version of quBNN that combines a QNN with an MLP, named as quilp. We evaluated all models on a multi-class dataset containing multimodal biosignals. We find that quMLP can outperform the classical MLP with minimal additional complexity, while quBNN maintains performance comparable with the classical MLP, with reduced computational complexity. We apply pruning and quantisation techniques to the QNN within the proposed quBNN model, aiming to further reduce its complexity. This compression yields substantial reductions in parameter and quantum gate count when compiled for quantum hardware, albeit with performance degradation.