#include <qneuron.hpp>

Public Member Functions
	QNeuron (QInterfacePtr reg, const std::vector< bitLenInt > &inputIndcs, bitLenInt outputIndx, QNeuronActivationFn activationFn=Sigmoid, real1_f alpha=ONE_R1_F, real1_f tol=FP_NORM_EPSILON/2)
	"QNeuron" is a "Quantum neuron" or "quantum perceptron" class that can learn and predict in superposition. More...

	QNeuron (const QNeuron &toCopy)
	Create a new QNeuron which is an exact duplicate of another, including its learned state. More...

QNeuron &	operator= (const QNeuron &toCopy)

void	SetAlpha (real1_f a)
	Set the "alpha" sharpness parameter of this QNeuron. More...

real1_f	GetAlpha ()
	Get the "alpha" sharpness parameter of this QNeuron. More...

void	SetActivationFn (QNeuronActivationFn f)
	Sets activation function enum. More...

QNeuronActivationFn	GetActivationFn ()
	Get activation function enum. More...

void	SetAngles (real1 *nAngles)
	Set the angles of this QNeuron. More...

void	GetAngles (real1 *oAngles)
	Get the angles of this QNeuron. More...

bitLenInt	GetInputCount ()

bitCapIntOcl	GetInputPower ()

real1_f	Predict (bool expected=true, bool resetInit=true)
	Predict a binary classification. More...

real1_f	Unpredict (bool expected=true)
	"Uncompute" the Predict() method More...

real1_f	LearnCycle (bool expected=true)

void	Learn (real1_f eta, bool expected=true, bool resetInit=true)
	Perform one learning iteration, training all parameters. More...

void	LearnPermutation (real1_f eta, bool expected=true, bool resetInit=true)
	Perform one learning iteration, measuring the entire QInterface and training the resulting permutation. More...

Protected Member Functions
real1_f	LearnInternal (bool expected, real1_f eta, bitCapIntOcl permOcl, real1_f startProb)

Static Protected Member Functions
static real1_f	applyRelu (real1_f angle)

static real1_f	negApplyRelu (real1_f angle)

static real1_f	applyGelu (real1_f angle)

static real1_f	negApplyGelu (real1_f angle)

static real1_f	applyAlpha (real1_f angle, real1_f alpha)

static real1_f	applyLeakyRelu (real1_f angle, real1_f alpha)

static real1_f	clampAngle (real1_f angle)

Protected Attributes
bitCapIntOcl	inputPower

bitLenInt	outputIndex

QNeuronActivationFn	activationFn

real1_f	alpha

real1_f	tolerance

std::vector< bitLenInt >	inputIndices

std::unique_ptr< real1[]>	angles

QInterfacePtr	qReg

Constructor & Destructor Documentation

◆ QNeuron() [1/2]

Qrack::QNeuron::QNeuron	(	QInterfacePtr	reg,
		const std::vector< bitLenInt > &	inputIndcs,
		bitLenInt	outputIndx,
		QNeuronActivationFn	activationFn = `Sigmoid`,
		real1_f	alpha = `ONE_R1_F`,
		real1_f	tol = `FP_NORM_EPSILON / 2`
	)

inline

"QNeuron" is a "Quantum neuron" or "quantum perceptron" class that can learn and predict in superposition.

This is a simple "quantum neuron" or "quantum perceptron" class, for use of the Qrack library for machine learning. See https://arxiv.org/abs/quant-ph/0410066 (and https://arxiv.org/abs/1711.11240) for the basis of this class' theoretical concept.

An untrained QNeuron (with all 0 variational parameters) will forward all inputs to 1/sqrt(2) * (|0> + |1>). The variational parameters are Pauli Y-axis rotation angles divided by 2 * Pi (such that a learning parameter of 0.5 will train from a default output of 0.5/0.5 probability to either 1.0 or 0.0 on one training input).

◆ QNeuron() [2/2]

Qrack::QNeuron::QNeuron ( const QNeuron & toCopy )

inline

Create a new QNeuron which is an exact duplicate of another, including its learned state.

Member Function Documentation

◆ applyAlpha()

static real1_f Qrack::QNeuron::applyAlpha	(	real1_f	angle,
		real1_f	alpha
	)

inlinestaticprotected

◆ applyGelu()

static real1_f Qrack::QNeuron::applyGelu ( real1_f angle )

inlinestaticprotected

◆ applyLeakyRelu()

static real1_f Qrack::QNeuron::applyLeakyRelu	(	real1_f	angle,
		real1_f	alpha
	)

inlinestaticprotected

◆ applyRelu()

static real1_f Qrack::QNeuron::applyRelu ( real1_f angle )

inlinestaticprotected

◆ clampAngle()

static real1_f Qrack::QNeuron::clampAngle ( real1_f angle )

inlinestaticprotected

◆ GetActivationFn()

QNeuronActivationFn Qrack::QNeuron::GetActivationFn ( )

inline

Get activation function enum.

◆ GetAlpha()

real1_f Qrack::QNeuron::GetAlpha ( )

inline

Get the "alpha" sharpness parameter of this QNeuron.

◆ GetAngles()

void Qrack::QNeuron::GetAngles ( real1 * oAngles )

inline

Get the angles of this QNeuron.

◆ GetInputCount()

bitLenInt Qrack::QNeuron::GetInputCount ( )

inline

◆ GetInputPower()

bitCapIntOcl Qrack::QNeuron::GetInputPower ( )

inline

◆ Learn()

void Qrack::QNeuron::Learn	(	real1_f	eta,
		bool	expected = `true`,
		bool	resetInit = `true`
	)

inline

Perform one learning iteration, training all parameters.

Inputs must be already loaded into "qReg" before calling this method. "expected" is the true binary output category, for training. "eta" is a volatility or "learning rate" parameter with a maximum value of 1.

In the feedback process of learning, default initial conditions forward untrained predictions to 1/sqrt(2) * (|0>

|1>) for the output bit. If you want to initialize other conditions before "Learn()," set "resetInit" to false.

◆ LearnCycle()

real1_f Qrack::QNeuron::LearnCycle ( bool expected = true )

inline

◆ LearnInternal()

real1_f Qrack::QNeuron::LearnInternal	(	bool	expected,
		real1_f	eta,
		bitCapIntOcl	permOcl,
		real1_f	startProb
	)

inlineprotected

◆ LearnPermutation()

void Qrack::QNeuron::LearnPermutation	(	real1_f	eta,
		bool	expected = `true`,
		bool	resetInit = `true`
	)

inline

Perform one learning iteration, measuring the entire QInterface and training the resulting permutation.

Inputs must be already loaded into "qReg" before calling this method. "expected" is the true binary output category, for training. "eta" is a volatility or "learning rate" parameter with a maximum value of 1.

In the feedback process of learning, default initial conditions forward untrained predictions to 1/sqrt(2) * (|0>

|1>) for the output bit. If you want to initialize other conditions before "LearnPermutation()," set "resetInit" to false.

◆ negApplyGelu()

static real1_f Qrack::QNeuron::negApplyGelu ( real1_f angle )

inlinestaticprotected

◆ negApplyRelu()

static real1_f Qrack::QNeuron::negApplyRelu ( real1_f angle )

inlinestaticprotected

◆ operator=()

QNeuron& Qrack::QNeuron::operator= ( const QNeuron & toCopy )

inline

◆ Predict()

real1_f Qrack::QNeuron::Predict	(	bool	expected = `true`,
		bool	resetInit = `true`
	)

inline

Predict a binary classification.

Feed-forward from the inputs, loaded in "qReg", to a binary categorical classification. "expected" flips the binary categories, if false. "resetInit," if true, resets the result qubit to 0.5/0.5 |0>/|1> superposition before proceeding to predict.

◆ SetActivationFn()

void Qrack::QNeuron::SetActivationFn ( QNeuronActivationFn f )

inline

Sets activation function enum.

◆ SetAlpha()

void Qrack::QNeuron::SetAlpha ( real1_f a )

inline

Set the "alpha" sharpness parameter of this QNeuron.

◆ SetAngles()

void Qrack::QNeuron::SetAngles ( real1 * nAngles )

inline

Set the angles of this QNeuron.

◆ Unpredict()

real1_f Qrack::QNeuron::Unpredict ( bool expected = true )

inline

"Uncompute" the Predict() method

Member Data Documentation

◆ activationFn

QNeuronActivationFn Qrack::QNeuron::activationFn

protected

◆ alpha

real1_f Qrack::QNeuron::alpha

protected

◆ angles

std::unique_ptr<real1[]> Qrack::QNeuron::angles

protected

◆ inputIndices

std::vector<bitLenInt> Qrack::QNeuron::inputIndices

protected

◆ inputPower

bitCapIntOcl Qrack::QNeuron::inputPower

protected

◆ outputIndex

bitLenInt Qrack::QNeuron::outputIndex

protected

◆ qReg

QInterfacePtr Qrack::QNeuron::qReg

protected

◆ tolerance

real1_f Qrack::QNeuron::tolerance

protected

The documentation for this class was generated from the following file:

include/qneuron.hpp

Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

◆ QNeuron() [1/2]

◆ QNeuron() [2/2]

Member Function Documentation

◆ applyAlpha()

◆ applyGelu()

◆ applyLeakyRelu()

◆ applyRelu()

◆ clampAngle()

◆ GetActivationFn()

◆ GetAlpha()

◆ GetAngles()

◆ GetInputCount()

◆ GetInputPower()

◆ Learn()

◆ LearnCycle()

◆ LearnInternal()

◆ LearnPermutation()

◆ negApplyGelu()

◆ negApplyRelu()

◆ operator=()

◆ Predict()

◆ SetActivationFn()

◆ SetAlpha()

◆ SetAngles()

◆ Unpredict()

Member Data Documentation

◆ activationFn

◆ alpha

◆ angles

◆ inputIndices

◆ inputPower

◆ outputIndex

◆ qReg

◆ tolerance