  Quantum computing is a promising candidate for accelerating machine learning
tasks. Limited by the control accuracy of current quantum hardware, reducing
the consumption of quantum resources is the key to achieving quantum advantage.
Here, we propose a quantum resonant dimension reduction (QRDR) algorithm based
on the quantum resonant transition to reduce the dimension of input data and
accelerate the quantum machine learning algorithms. After QRDR, the dimension
of input data $N$ can be reduced into desired scale $R$, and the effective
information of the original data will be preserved correspondingly, which will
reduce the computational complexity of subsequent quantum machine learning
algorithms or quantum storage. QRDR operates with polylogarithmic time
complexity and reduces the error dependency from the order of $1/\epsilon^3$ to
the order of $1/\epsilon$, compared to existing algorithms. We demonstrate the
performance of our algorithm combining with two types of quantum classifiers,
quantum support vector machines and quantum convolutional neural networks, for
classifying underwater detection targets and quantum many-body phase
respectively. The simulation results indicate that reduced data improved the
processing efficiency and accuracy following the application of QRDR. As
quantum machine learning continues to advance, our algorithm has the potential
to be utilized in a variety of computing fields.
