QuantumMNIST256Classifier {QuantumOps}

QuantumMNIST256Classifier

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)