812 research outputs found
Quantum machine learning: a classical perspective
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 successes, 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.Comment: v3 33 pages; typos corrected and references adde
An Algorithm for Training Polynomial Networks
We consider deep neural networks, in which the output of each node is a
quadratic function of its inputs. Similar to other deep architectures, these
networks can compactly represent any function on a finite training set. The
main goal of this paper is the derivation of an efficient layer-by-layer
algorithm for training such networks, which we denote as the \emph{Basis
Learner}. The algorithm is a universal learner in the sense that the training
error is guaranteed to decrease at every iteration, and can eventually reach
zero under mild conditions. We present practical implementations of this
algorithm, as well as preliminary experimental results. We also compare our
deep architecture to other shallow architectures for learning polynomials, in
particular kernel learning
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