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Quantum Machine Learning

An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples

arXiv
Authors: Siddharth Dangwal, Ritvik Sharma, Debanjan Bhowmik

Year

2020

Paper ID

18911

Status

Preprint

Abstract Read

~2 min

Abstract Words

107

Citations

N/A

Abstract

We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2011.14297 [2] arXiv https://arxiv.org/abs/2011.14297 [3] Publisher https://arxiv.org/abs/2011.14297

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal

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