  In the past decade, remarkable progress has been achieved in deep learning
related systems and applications. In the post Moore's Law era, however, the
limit of semiconductor fabrication technology along with the increasing data
size have slowed down the development of learning algorithms. In parallel, the
fast development of quantum computing has pushed it to the new ear. Google
illustrates quantum supremacy by completing a specific task (random sampling
problem), in 200 seconds, which is impracticable for the largest classical
computers. Due to the limitless potential, quantum based learning is an area of
interest, in hopes that certain systems might offer a quantum speedup. In this
work, we propose a novel architecture QuClassi, a quantum neural network for
both binary and multi-class classification. Powered by a quantum
differentiation function along with a hybrid quantum-classic design, QuClassi
encodes the data with a reduced number of qubits and generates the quantum
circuit, pushing it to the quantum platform for the best states, iteratively.
We conduct intensive experiments on both the simulator and IBM-Q quantum
platform. The evaluation results demonstrate that QuClassi is able to
outperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum and
QuantumFlow by up to 53.75% and 203.00% for binary and multi-class
classifications. When comparing to traditional deep neural networks, QuClassi
achieves a comparable performance with 97.37% fewer parameters.
