4,614 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
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Koopman Operator learning for Accelerating Quantum Optimization and Machine Learning
Finding efficient optimization methods plays an important role for quantum
optimization and quantum machine learning on near-term quantum computers. While
backpropagation on classical computers is computationally efficient, obtaining
gradients on quantum computers is not, because the computational complexity
usually scales with the number of parameters and measurements. In this paper,
we connect Koopman operator theory, which has been successful in predicting
nonlinear dynamics, with natural gradient methods in quantum optimization. We
propose a data-driven approach using Koopman operator learning to accelerate
quantum optimization and quantum machine learning. We develop two new families
of methods: the sliding window dynamic mode decomposition (DMD) and the neural
DMD for efficiently updating parameters on quantum computers. We show that our
methods can predict gradient dynamics on quantum computers and accelerate the
variational quantum eigensolver used in quantum optimization, as well as
quantum machine learning. We further implement our Koopman operator learning
algorithm on a real IBM quantum computer and demonstrate their practical
effectiveness
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