Quantum-classical Hybrid Network Outperforms Conventional Ones

A team at Terra Quantum AG has designed a parallel hybrid quantum neural network, which is a combination of a quantum layer and a classical layer. This architecture enables them to learn complicated patterns and relationships from data inputs more easily than traditional machine learning methods. The authors demonstrated that their model is “a powerful tool for quantum machine learning” by training it on two periodic datasets with high-frequency noise added. The results showed that the hybrid model outperformed either its quantum layer or its classical layer, producing better predictions and being more adaptable to complex problems and new datasets.