Enhanced Superposed Parameterized Quantum Neural Network Classifier for richer nonlinear decision boundaries and barren plateau mitigation

Quantum neural networks (QNNs) offer higher-dimensional spaces than classical neural networks, giving them potential to capture deeper relationships in data. In practice, however, a QNN's expressivity is often bounded by the nonlinearity of its feature map (the data-embedding circuit). Superposed Parameterized Quantum Circuits (SPQCs) address this weakness by introducing a single nonlinear term after the feature map, enabling nonlinear transformations during training. We propose an enhanced SPQC with an added qubit register to introduce multiple nonlinear terms. The number of added terms scales linearly with the size of this register, yielding a controllable increase in model expressivity. We present our circuit design and report empirical results on various tasks, comparing the enhanced SPQC's performance to the original SPQC design and traditional QNNs.