Large-scale quantum machine learning
- URL: http://arxiv.org/abs/2108.01039v1
- Date: Mon, 2 Aug 2021 17:00:18 GMT
- Title: Large-scale quantum machine learning
- Authors: Tobias Haug, Chris N. Self, M. S. Kim
- Abstract summary: We measure quantum kernels using randomized measurements to gain a quadratic speedup in time and quickly process large datasets.
We efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth.
Using currently available quantum computers, the MNIST database can be processed within 220 hours instead of 10 years.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 to gain a quadratic speedup in computation time and
quickly process large datasets. 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. We
demonstrate the advantages and speedups of our methods by classifying images
with the IBM quantum computer. Our approach is exceptionally robust to noise
via a complementary error mitigation scheme. Using currently available quantum
computers, the MNIST database can be processed within 220 hours instead of 10
years which opens up industrial applications of quantum machine learning.
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