Publication date
May 2022
DOIAbstract
Circuit-encoded FashionMNIST dataset and sample loading script from the paper "Data compression for quantum machine learning.
Circuit-encoded FashionMNIST dataset and sample loading script from the paper "Data compression for quantum machine learning.
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
We present a method for optimizing quantum circuits architecture, based on the notion of a quantum c...
Quantum computing (QC) aims to solve certain computational problems beyond the capabilities of even ...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
The data representation in a machine-learning model strongly influences its performance. This become...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
Experimental and numerical data for figures supporting the article "Protecting Expressive Circuits w...
This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability u...
Quantum information and machine learning are two highly active research fields in the modern scienti...
This repository provides the source code for some analytical and numerical implementations of the Ph...
Encoding classical data into a quantum is some of the most crucial steps to get to a fully functioni...
We apply the framework of block-encodings, introduced by Low and Chuang (under the name standard-for...
This project is one of the Qiskit mentorship programs to replicate two papers arXiv:1905.10876 and a...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
We present a method for optimizing quantum circuits architecture, based on the notion of a quantum c...
Quantum computing (QC) aims to solve certain computational problems beyond the capabilities of even ...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
The data representation in a machine-learning model strongly influences its performance. This become...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of ...
Experimental and numerical data for figures supporting the article "Protecting Expressive Circuits w...
This is supplemental data and code for: D. Hothem et al., Learning a quantum computer's capability u...
Quantum information and machine learning are two highly active research fields in the modern scienti...
This repository provides the source code for some analytical and numerical implementations of the Ph...
Encoding classical data into a quantum is some of the most crucial steps to get to a fully functioni...
We apply the framework of block-encodings, introduced by Low and Chuang (under the name standard-for...
This project is one of the Qiskit mentorship programs to replicate two papers arXiv:1905.10876 and a...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
We present a method for optimizing quantum circuits architecture, based on the notion of a quantum c...
Quantum computing (QC) aims to solve certain computational problems beyond the capabilities of even ...
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