Quantum-enhanced bosonic learning machine
- URL: http://arxiv.org/abs/2104.04168v1
- Date: Fri, 9 Apr 2021 02:44:57 GMT
- Title: Quantum-enhanced bosonic learning machine
- Authors: Chi-Huan Nguyen, Ko-Wei Tseng, Gleb Maslennikov, H. C. J. Gan, Dzmitry
Matsukevich
- Abstract summary: We show a quantum-enhanced bosonic learning machine operating on quantum data with a system of trapped ions.
We implement the unsupervised K-means algorithm to recognize a pattern in a set of high-dimensional quantum states.
We use the discovered knowledge to classify unknown quantum states with the supervised k-NN algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum processors enable computational speedups for machine learning through
parallel manipulation of high-dimensional vectors. Early demonstrations of
quantum machine learning have focused on processing information with qubits. In
such systems, a larger computational space is provided by the collective space
of multiple physical qubits. Alternatively, we can encode and process
information in the infinite-dimensional Hilbert space of bosonic systems such
as quantum harmonic oscillators. This approach offers a hardware-efficient
solution with potential quantum speedups to practical machine learning
problems. Here we demonstrate a quantum-enhanced bosonic learning machine
operating on quantum data with a system of trapped ions. Core elements of the
learning processor are the universal feature-embedding circuit that encodes
data into the motional states of ions, and the constant-depth circuit that
estimates overlap between two quantum states. We implement the unsupervised
K-means algorithm to recognize a pattern in a set of high-dimensional quantum
states and use the discovered knowledge to classify unknown quantum states with
the supervised k-NN algorithm. These results provide building blocks for
exploring machine learning with bosonic processors.
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