Quantum classifier with tailored quantum kernel

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Kernel methods have a wide spectrum of applications in machine learning. Recently, a link between quantum computing and kernel theory has been formally established, opening up opportunities for quantum techniques to enhance various existing machine-learning methods. We present a distance-based quantum classifier whose kernel is based on the quantum state fidelity between training and test data. The quantum kernel can be tailored systematically with a quantum circuit to raise the kernel to an arbitrary power and to assign arbitrary weights to each training data. Given a specific input state, our protocol calculates the weighted power sum of fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements, requiring only a constant number of repetitions regardless of the number of data. We also show that our classifier is equivalent to measuring the expectation value of a Helstrom operator, from which the well-known optimal quantum state discrimination can be derived. We demonstrate the performance of our classifier via classical simulations with a realistic noise model and proof-of-principle experiments using the IBM quantum cloud platform.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2020-05
Language
English
Article Type
Article
Citation

NPJ QUANTUM INFORMATION, v.6, no.1, pp.41

ISSN
2056-6387
DOI
10.1038/s41534-020-0272-6
URI
http://hdl.handle.net/10203/274703
Appears in Collection
EE-Journal Papers(저널논문)
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