QuantumOps

Performs Common Linear Algebra Operations Used in Quantum Computing and Implements Quantum Algorithms

Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help in practicing the linear algebra involved in quantum operations. Has functionality for the creation of arbitrarily sized kets, bras, matrices and implements quantum gates, inner products, and tensor products. Contains all commonly used quantum gates, creates arbitrarily controlled versions of all gates, and can simulate complete or partial measurements of kets. Implements modular arithmetic commonly found in quantum algorithms and can convert functions into equivalent quantum gates. It can also simulate larger applications, including Steane error correction <DOI:10.1103/physrevlett.77.793>, the Quantum Fourier Transform developed by Shor (1999), Shor's algorithm which factors numbers up to 21, Grover's algorithm (1996), the Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and the training of a quantum neural network developed by Schuld (2018) <arXiv:1804.00633> which can classify the MNIST dataset.

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Description file content

Package
QuantumOps
Title
Performs Common Linear Algebra Operations Used in Quantum Computing and Implements Quantum Algorithms
Version
2.5.2
Date
2019-5-5
Author
Salonik Resch
Maintainer
Salonik Resch
Description
Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help in practicing the linear algebra involved in quantum operations. Has functionality for the creation of arbitrarily sized kets, bras, matrices and implements quantum gates, inner products, and tensor products. Contains all commonly used quantum gates, creates arbitrarily controlled versions of all gates, and can simulate complete or partial measurements of kets. Implements modular arithmetic commonly found in quantum algorithms and can convert functions into equivalent quantum gates. It can also simulate larger applications, including Steane error correction , the Quantum Fourier Transform developed by Shor (1999), Shor's algorithm which factors numbers up to 21, Grover's algorithm (1996), the Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) , and the training of a quantum neural network developed by Schuld (2018) which can classify the MNIST dataset.
Depends
R (>= 3.1.0)
License
GPL-3
RoxygenNote
5.0.1
NeedsCompilation
no
Packaged
2019-05-06 04:36:27 UTC; mike
Repository
CRAN
Date/Publication
2019-05-06 07:20:22 UTC

install.packages('QuantumOps')

2.5.2

a month ago

Salonik Resch

GPL-3

Depends on

R (>= 3.1.0)

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