  Deep neural network powered artificial intelligence has rapidly changed our
daily life with various applications. However, as one of the essential steps of
deep neural networks, training a heavily weighted network requires a tremendous
amount of computing resources. Especially in the post-Moore's Law era, the
limit of semiconductor fabrication technology has restricted the development of
learning algorithms to cope with the increasing high-intensity training data.
Meanwhile, quantum computing has demonstrated its significant potential in
terms of speeding up the traditionally compute-intensive workloads. For
example, Google illustrated quantum supremacy by completing a sampling
calculation task in 200 seconds, which is otherwise impracticable on the
world's largest supercomputers. To this end, quantum-based learning has become
an area of interest, with the potential of a quantum speedup. In this paper, we
propose GenQu, a hybrid and general-purpose quantum framework for learning
classical data through quantum states. We evaluate GenQu with real datasets and
conduct experiments on both simulations and real quantum computer IBM-Q. Our
evaluation demonstrates that, compared with classical solutions, the proposed
models running on GenQu framework achieve similar accuracy with a much smaller
number of qubits, while significantly reducing the parameter size by up to
95.86% and converging speedup by 33.33% faster.
