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In the past decade, remarkable progress has been achieved in deep learningrelated systems and applications. In the post Moore's Law era, however, thelimit of semiconductor fabrication technology along with the increasing datasize have slowed down the development of learning algorithms. In parallel, thefast development of quantum computing has pushed it to the new ear. Googleillustrates quantum supremacy by completing a specific task (random samplingproblem), in 200 seconds, which is impracticable for the largest classicalcomputers. Due to the limitless potential, quantum based learning is an area ofinterest, in hopes that certain systems might offer a quantum speedup. In thiswork, we propose a novel architecture QuClassi, a quantum neural network forboth binary and multi-class classification. Powered by a quantumdifferentiation function along with a hybrid quantum-classic design, QuClassiencodes the data with a reduced number of qubits and generates the quantumcircuit, pushing it to the quantum platform for the best states, iteratively.We conduct intensive experiments on both the simulator and IBM-Q quantumplatform. The evaluation results demonstrate that QuClassi is able tooutperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum andQuantumFlow by up to 53.75% and 203.00% for binary and multi-classclassifications. When comparing to traditional deep neural networks, QuClassiachieves a comparable performance with 97.37% fewer parameters.

QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity