QuantumMNIST256Classifier: QuantumMNIST256Classifier

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

Description

Quantum classifier which was proposed by Maria Schuld (2018). Consists of 33 quantum gates with a depth of 19. Takes as input samples with dimensions of 256 and a corresponding list of labels indicating the correct output value of each sample. Will update the parameters of the gates in order to correctly identify one of the digits specified.

Usage

QuantumMNIST256Classifier(
	data=NULL,labels=NULL,digit=0,
	eta=1,decay=1,bsc=1,t=20,tag="",pl=TRUE,train=TRUE,
	validT=FALSE,vdata=NULL,vlabels=NULL,
	pretrained=FALSE,alpha=NULL,beta=NULL,gamma=NULL)

Arguments

data	matrix containing input training data. Rows are individual samples. There must be 256 columns
labels	Vector containing labels of digits. Length must be the same as the number of rows in data
digit	Individual digit (0-9) to identify
eta	learning rate for parameter updates
decay	Multiplier for learning rate after each training iteration. If set to less than 1, the learning rate decays in time
bsc	Scaler for the learning rate of the bias. Setting to a low value will result in other parameters updating faster than the bias
t	Number of training iterations to perform. Total runs is equal to this value multiplied by the number of samples provided
tag	String to attach to name of output files
pl	Boolean indicating whether training output should be plotted
train	Boolean specifying if network should trained on training data, only false if passing in pretrained model
validT	Boolean specifying if the network should be tested on validation data while being trained
vdata	Validation data, necessary if validT is set to true in which case network is tested on this data while being trained. Can be set to same as data.
vlabels	Validation labels, necessary if validT is set to true in which case network is tested on vdata while being trained. Can be set to same as labels.
pretrained	Boolean specifying if a pretrained model is being passed in. If so, alpha, beta, and gamme will be set to intputs, rather than randomized.
alpha	alpha values for gates if pretrained is set to TRUE, should be a vector of length 33
beta	beta values for gates if pretrained is set to TRUE, should be a vector of length 33
gamma	gamma values for gates if pretrained is set to TRUE, should be a vector of length 33

Value

List containing a list of the 33 gates and the matrix representing the entire circuit of the trained classifier

Examples

 ## Not run: 
 QuantumMNIST256Classifier(matrix(sample(256,replace=TRUE),nrow=1),array(1),0,1,1,.001,1,"test")
 
## End(Not run)

QuantumOps documentation built on Feb. 3, 2020, 5:07 p.m.