QuantumClassifier {QuantumOps}

QuantumClassifier

Description

Quantum classifier which was proposed by Maria Schuld (2018). Consists of code blocks which have parallel single qubit quantum gates followed by controlled qubit gates. Takes as input samples 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 the samples.

Usage

QuantumClassifier(	n=8,B=2,r=c(1,3),
			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,bias=NULL,
		       	writeParameters=FALSE,outputPath=NULL	)

Arguments

n

Number of qubits that this the classifier will use.

B

Number of blocks in the circuit. A block consists of n single qubit gates applied to each of the qubits in parallel and then n/(gcd,r) controlled qubit gates to perform a maximal entanglement. r is the specified range of the controlled gates

r

Vector containing the range for each block. length(r) should be equal to B. r of 1 means controlled gates are performed on adjacent qubits

data

matrix containing input training data. Rows are individual samples. The number of columns should be equal to 2^n

labels

Vector containing labels of digits. Length must be the same as the number of rows in data

digit

Individual output to identify. The network will attempt to differentiate between inputs that are labelled as digit (in the labels vector) and inputs that are labelled as any other number

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 equal to the number of gates in the circuit.

beta

beta values for gates if pretrained is set to TRUE, should be a vector of length equal to the number of gates in the circuit.

gamma

gamma values for gates if pretrained is set to TRUE, should be a vector of length equal to the number of gates in the circuit.

bias

Bias applied to the output of the circuit.

writeParameters

Boolean specifying whether function should write the parameters as it trains. Useful when training takes a long time.

outputPath

String which specifies path to write output parameters to if writeParameters is TRUE. Must have write priveleges in this directory. The function will create two directories inside outputPath, named 0 and 1, and will alternate output to each folder. This prevents corruption of output if interrupted.

Value

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

Examples

 ## Not run: 
 QuantumClassifier(n=8,B=2,r=c(1,3),
    matrix(sample(256,replace=TRUE),nrow=1),
	array(1),0,1,1,.001,1,"test")
 
## End(Not run)