Journal
INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS
Volume 59, Issue 10, Pages 3134-3144Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10773-020-04567-1
Keywords
Quantum algorithm; Quantum machine learning; Similarity measurement; Euclidean distance
Categories
Funding
- National Natural Science Foundation of China [61976053, 61772134]
- Fujian Province Natural Science Foundation [2018J01776]
- Program for New Century Excellent Talents in Fujian Province University
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Similarity measurement is a fundamental problem that arise both on its own and as a key subroutine in more complex tasks, such as machine learning. However, in classical algorithms, the time used to similarity measurement usually increases exponentially as the amount of data and the number of data dimensions increase. In this paper, we presented three quantum algorithms based on Euclidean distance to measure the similarity between data sets. In the proposed algorithms, some special unitary operations are utilized to construct imperative quantum states from quantum random access memory. Then, a badly needed result for estimating the similarity between data sets, can be got by performing projective measurements. Furthermore, it is shown that these algorithms can achieve the exponential acceleration of the classical algorithm in the quantity or the dimension of data.
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