Quantum machine learning of large datasets using randomized measurements
Quantum computers promise to enhance machine learning for practical
applications. Quantum machine learning for real-world data has to handle
extensive amounts of high-dimensional data. However, conventional methods for
measuring quantum kernels are impractical for large datasets as they scale with
the square of the dataset size. Here, we measure quantum kernels using
randomized measurements. The quantum computation time scales linearly with
dataset size and quadratic for classical post-processing. While our method
scales in general exponentially in qubit number, we gain a substantial speed-up
when running on intermediate-sized quantum computers. Further, we efficiently
encode high-dimensional data into quantum computers with the number of features
scaling linearly with the circuit depth. The encoding is characterized by the
quantum Fisher information metric and is related to the radial basis function
kernel. Our approach is robust to noise via a cost-free error mitigation
scheme. We demonstrate the advantages of our methods for noisy quantum
computers by classifying images with the IBM quantum computer. To achieve
further speedups we distribute the quantum computational tasks between
different quantum computers. Our method enables benchmarking of quantum machine
learning algorithms with large datasets on currently available quantum
computers.
Tobias Haug Chris N. Self M. S. Kim