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23 April 2024 |
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Article overview
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Data compression for quantum machine learning | | Rohit Dilip
; Yu-Jie Liu
; Adam Smith
; Frank Pollmann
; | | Date: |
24 Apr 2022 | | Abstract: | The advent of noisy-intermediate scale quantum computers has introduced the
exciting possibility of achieving quantum speedups in machine learning tasks.
These devices, however, are composed of a small number of qubits, and can
faithfully run only short circuits. This puts many proposed approaches for
quantum machine learning beyond currently available devices. We address the
problem of efficiently compressing and loading classical data for use on a
quantum computer. Our proposed methods allow both the required number of qubits
and depth of the quantum circuit to be tuned. We achieve this by using a
correspondence between matrix-product states and quantum circuits, and further
propose a hardware-efficient quantum circuit approach, which we benchmark on
the Fashion-MNIST dataset. Finally, we demonstrate that a quantum circuit based
classifier can achieve competitive accuracy with current tensor learning
methods using only 11 qubits. | | Source: | arXiv, 2204.11170 | | Services: | Forum | Review | PDF | Favorites | |
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