a rigorous and robust quantum speed-up in supervised machine learning

A rigorous and robust quantum speed-up in supervised.. Finding potential applications for quantum computing that demonstrate quantum speed-ups is a central goal of the field. Much attention has been directed towards establishing.

A rigorous and robust quantum speed-up in supervised. from www.quandco.com

A rigorous and robust quantum speed-up in supervised machine learning. Yunchao Liu, Srinivasan Arunachalam, Kristan Temme. Over the past few years several.

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A rigorous quantum speed-up for supervised classification using a quantum learning algorithm that only requires classical access to data and achieves high accuracy,.

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Recently, several quantum machine learning algorithms have been proposed that may offer quantum speed-ups over their classical counterparts. Most of these algorithms are either.

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Over the past few years several quantum machine learning algorithms were proposed that promise quantum speed-ups over their classical counterparts. Most of these learning.

A rigorous and robust quantum speed-up in supervised machine.

In this paper, we establish a rigorous quantum speed-up for supervised classification using a general-purpose quantum learning algorithm that only requires classical access to data. Our.

A rigorous and robust quantum speed-up in supervised machine.

A rigorous and robust quantum speed-up in supervised machine learning YunchaoLiu (UC Berkeley). Machine learning of high dimensional data on a noisy quantum processor, arxiv:.

A Rigorous and Robust Quantum Speed DocsLib

A rigorous and robust quantum speed-up in supervised machine learning Yunchao Liu (UC Berkeley) Joint work with Srinivasan Arunachalam and Kristan Temme (IBM

A rigorous and robust quantum speed-up in supervised machine.

In this paper, we establish a rigorous quantum speed-up for supervised classification using a quantum learning algorithm that only requires classical access to data..

A Rigorous and Robust Quantum Speed-up in Supervised.

Yunchao Liu (UC Berkeley)https://simons.berkeley.edu/talks/rigorous-and-robust-quantum-speed-supervised-machine-learningQuantum Wave in Computing Reunion

Study demonstrates the quantum speed up of supervised machine.

More information: Yunchao Liu et al, A rigorous and robust quantum speed-up in supervised machine learning, Nature Physics (2021). DOI: 10.1038/s41567-021-01287-z

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In this paper, we establish a rigorous quantum speed-up for supervised classification using a quantum learning algorithm that only requires classical access to data..

Source: dwzke5c1hcizv.cloudfront.net

A rigorous and robust quantum speed-up in supervised machine learning. Yunchao Liu, Srinivasan Arunachalam, Kristan Temme. Over the past few years several.

Kristan Temme: "A rigorous and robust quantum speed-up in.

Entropy Inequalities, Quantum Information and Quantum Physics 2021"A rigorous and robust quantum speed-up in supervised machine learning"Kristan Temme IBM.

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Here we construct a formal learning problem and prove that the recently proposed heuristic primitive -- quantum kernel methods -- has a rigorous end-to-end quantum speed.

A rigorous and robust quantum speed-up in supervised machine.

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A rigorous and robust quantum speed-up in supervised machine.

A rigorous and robust quantum speed-up in supervised machine learning. Recently, several quantum machine learning algorithms have been proposed that may offer quantum speed.

Source: dwzke5c1hcizv.cloudfront.net

The resulting quantum classifier achieves high accuracy and is robust against additive errors in the kernel entries that arise from finite sampling statistics. Many quantum.