Journal
PHYSICAL REVIEW A
Volume 98, Issue 3, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.98.032309
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Funding
- JST PRESTO [JP-MJPR1666, JPMJPR1668]
- JST CREST [JPMJCR1672, JPMJCR1673]
- KAKENHI [16H02211]
- JST ERATO [JPM-JER1601]
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We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer formachine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
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