Focus on Quantum Machine Learning

Photo credit: iStock/Jakarin2521.

Gian Giacomo Guerreschi, Intel, USA
Roger Melko, University of Waterloo and the Perimeter Institute for Theoretical Physics, Canada
Nathan Wiebe, Microsoft, USA

Two of the most exciting areas of scientific and technological progress today centre around powerful advances in machine learning, and a rapid arms-race to build quantum hardware. The integration of the two fields is at the heart of a new interdisciplinary research effort, quantum machine learning. While there is a staggering yet unknown potential for disruptive advances, many fundamental challenges to combining these two technologies exist. This issue forms a very select resource spanning the very latest cutting edge research in this rapidly developing field. Through a single high quality collection, readers across the world will be able to see the current "state of the science" in an interdisciplinary area of growing importance within the machine learning and quantum technology communities. Specific topics to be covered include:

  • Classical machine learning applied to quantum systems;
  • Algorithms for machine learning on quantum hardware;
  • Machine learning for quantum experiments;
  • Frontiers in quantum machine learning.

The articles listed below are the first accepted contributions to the collection and further additions will appear on an ongoing basis.

Papers

Quantum algorithm for the nonlinear dimensionality reduction with arbitrary kernel

YaoChong Li et al 2021 Quantum Sci. Technol. 6 014001

Open access
Quantum implementation of an artificial feed-forward neural network

Francesco Tacchino et al 2020 Quantum Sci. Technol. 5 044010

A quantum deep convolutional neural network for image recognition

YaoChong Li et al 2020 Quantum Sci. Technol. 5 044003

Machine learning design of a trapped-ion quantum spin simulator

Yi Hong Teoh et al 2020 Quantum Sci. Technol. 5 024001

Open access
Visual assessment of multi-photon interference

Fulvio Flamini et al 2019 Quantum Sci. Technol. 4 024008

Machine learning method for state preparation and gate synthesis on photonic quantum computers

Juan Miguel Arrazola et al 2019 Quantum Sci. Technol. 4 024004

Towards quantum machine learning with tensor networks

William Huggins et al 2019 Quantum Sci. Technol. 4 024001

Quantum autoencoders via quantum adders with genetic algorithms

L Lamata et al 2019 Quantum Sci. Technol. 4 014007

Quantum variational autoencoder

Amir Khoshaman et al 2019 Quantum Sci. Technol. 4 014001

Deep neural decoders for near term fault-tolerant experiments

Christopher Chamberland and Pooya Ronagh 2018 Quantum Sci. Technol. 3 044002

Heterogeneous quantum computing for satellite constellation optimization: solving the weighted k-clique problem

Gideon Bass et al 2018 Quantum Sci. Technol. 3 024010

Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices

Marcello Benedetti et al 2018 Quantum Sci. Technol. 3 034007

Learning relevant features of data with multi-scale tensor networks

E Miles Stoudenmire 2018 Quantum Sci. Technol. 3 034003

Learning relevant features of data with multi-scale tensor networks

E Miles Stoudenmire 2018 Quantum Sci. Technol. 3 034003

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