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An Introduction to Quantum Computing for Statisticians and Data Scientists

arXiv 2112.06587 ↗First posted 13 Dec 2021stat.CO cs.LGcited by 1

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Abstract (arXiv)

Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fertilizer design, supply chain and traffic optimization, or optimization for machine learning tasks. Because quantum computers function fundamentally differently from classical computers, the emergence of quantum computing technology will lead to a new evolutionary branch of statistical and data analytics methodologies. This review provides an introduction to quantum computing designed to be accessible to statisticians and data scientists, aiming to equip them with an overarching framework of quantum computing, the basic language and building blocks of quantum algorithms, and an overview of existing quantum applications in statistics and data analysis. Our goal is to enable statisticians and data scientists to follow quantum computing literature relevant to their fields, to collaborate with quantum algorithm designers, and, ultimately, to bring forth the next generation of statistical and data analytics tools.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 200 references that are in the map. First posted on arXiv 13 Dec 2021.

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27 of 200 references.

References · 27

  1. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  2. 2020Variational Quantum Algorithmscited by 692
  3. 2018Barren plateaus in quantum neural network training landscapescited by 630
  4. 2019Benign Overfitting in Linear Regressioncited by 549
  5. 2020The power of quantum neural networkscited by 379
  6. 2020Power of data in quantum machine learningcited by 338
  7. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  8. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  9. 2017Quantum machine learning: a classical perspectivecited by 95
  10. 2020Recurrent Quantum Neural Networkscited by 80
  11. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  12. 2016Quantum Recommendation Systemscited by 70
15 more
  1. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  2. 2016Efficient quantum tomography IIcited by 54
  3. 2019Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learningcited by 37
  4. 2019Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithmscited by 31
  5. 2018The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulationcited by 30
  6. 2014Quantum Inference on Bayesian Networkscited by 19
  7. 2018Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimensioncited by 18
  8. 2020Adiabatic Quantum Linear Regressioncited by 17
  9. 2018Quantum linear systems algorithms: a primercited by 14
  10. 2019Geometry of learning neural quantum statescited by 14
  11. 2019Quantum algorithm for estimating volumes of convex bodiescited by 8
  12. 2019Time-dependent Hamiltonian simulation with L1-norm scalingcited by 7
  13. 2017A Quantum Extension of Variational Bayes Inferencecited by 5
  14. 2020Low depth mechanisms for quantum optimizationcited by 5
  15. 2018A quantum algorithm for simulating non-sparse Hamiltonianscited by 2

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  1. 2024Quantum natural gradient without monotonicitycited by 0