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16 April 2024 |
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Article overview
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Quantum machine learning: a classical perspective | | Carlo Ciliberto
; Mark Herbster
; Alessandro Davide Ialongo
; Massimiliano Pontil
; Andrea Rocchetto
; Simone Severini
; Leonard Wossnig
; | | Date: |
26 Jul 2017 | | Abstract: | Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these impressive results, the proximity to the physical
limits of chip fabrication alongside the increasing size of datasets are
motivating a growing number of researchers to explore the possibility of
harnessing the power of quantum computation to speed-up classical machine
learning algorithms. Here we review the literature in quantum machine learning
and discuss perspectives for a mixed readership of classical machine learning
and quantum computation experts. Particular emphasis will be placed on
clarifying the limitations of quantum algorithms, how they compare with their
best classical counterparts and why quantum resources are expected to provide
advantages for learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed. | | Source: | arXiv, 1707.8561 | | Services: | Forum | Review | PDF | Favorites | |
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