• Corpus ID: 237485462

Quadratic Quantum Speedup for Perceptron Training

  title={Quadratic Quantum Speedup for Perceptron Training},
  author={Pengcheng Liao and Barry C. Sanders and Tim Byrnes},
Pengcheng Liao, Barry C. Sanders, 2, 3, ∗ and Tim Byrnes 5, 6, 7, 8, † Institute for Quantum Science and Technology, University of Calgary, Alberta, T2N 1N4, Canada Shanghai Branch, National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Shanghai 201315, China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China New York… 

Figures from this paper

Quantum Perceptron Revisited: Computational-Statistical Tradeoffs

This paper introduces a hybrid quantum-classical perceptron algorithm with lower complexity and better generalization ability than the classical perceptron, and shows a quadratic improvement over the Classical perceptron in both the number of samples and the margin of the data.



Quantum Computation and Quantum Information (10th Anniversary edition)

Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering.

Algorithms – ESA 2012

This paper discusses some of the aspects of the design, analysis and engineering of Big Data algorithms, including massive parallelism, streaming algorithms, sketches and synopses, cloud technologies, and more, and reflects on their evolution.

The Elements of Statistical Learning

Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.

and K

  • Svore, in Advances in 9 Neural Information Processing Systems
  • 2016

and P


    • Rev. Lett. 122, 040504
    • 2019


    • Rev. Lett. 113, 130503
    • 2014


    • Lett. A 379, 660
    • 2015

    and D

    • Tao, arXiv:1809.06056
    • 2018

    Information Sciences 128

    • 257
    • 2000