  Universality of neural networks describes the ability to approximate
arbitrary function, and is a key ingredient to keep the method effective. The
established models for universal quantum neural networks(QNN), however, require
the preparation of multiple copies of the same quantum state to generate the
nonlinearity, with the copy number increasing significantly for highly
oscillating functions, resulting in a huge demand for a large-scale quantum
processor. To address this problem, we propose a new QNN model that harbors
universality without the need of multiple state-duplications, and is more
likely to get implemented on near-term devices. To demonstrate the
effectiveness, we compare our proposal with two popular QNN models in solving
typical supervised learning problems. We find that our model requires
significantly fewer qubits and it outperforms the other two in terms of
accuracy and relative error.
