#include <qneuron.hpp>

Public Member Functions
	QNeuron (QInterfacePtr reg, const std::vector< bitLenInt > &inputIndcs, const bitLenInt &outputIndx)
	"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= (QNeuron &toCopy)

void	SetSimulator (QInterfacePtr sim)
	Replace the simulator. More...

void	SetIndices (const std::vector< bitLenInt > &inputIndcs, const bitLenInt &outputIndx)
	Replace the input and output indices. More...

QInterfacePtr	GetSimulator ()
	Retrieve the simulator. More...

bitLenInt	GetInputCount ()

bitCapIntOcl	GetInputPower ()

real1_f	Predict (const real1_s *angles, const bool &expected=true, const bool &resetInit=true, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)
	Predict a binary classification. More...

real1_f	Unpredict (const real1_s *angles, const bool &expected=true, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)
	"Uncompute" the Predict() method More...

real1_f	LearnCycle (real1_s *angles, const bool &expected=true, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)

void	Learn (real1_s *angles, const real1_f &eta, const bool &expected=true, const bool &resetInit=true, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)
	Perform one learning iteration, training all parameters. More...

void	LearnPermutation (real1_s *angles, const real1_f &eta, const bool &expected=true, const bool &resetInit=true, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)
	Perform one learning iteration, measuring the entire QInterface and training the resulting permutation. More...

Protected Member Functions
real1_f	LearnInternal (real1_s *angles, const bool &expected, const real1_f &eta, const bitCapIntOcl &permOcl, const real1_f &startProb, const QNeuronActivationFn &activationFn=Sigmoid, const real1_f &alpha=ONE_R1_F)

Static Protected Member Functions
static real1_f	applyRelu (const real1_f &angle)

static real1_f	negApplyRelu (const real1_f &angle)

static real1_f	applyGelu (const real1_f &angle)

static real1_f	negApplyGelu (const real1_f &angle)

static real1_f	applyAlpha (real1_f angle, const real1_f &alpha)

static real1_f	applyLeakyRelu (const real1_f &angle, const real1_f &alpha)

static real1_f	clampAngle (real1_f angle)

Protected Attributes
bitLenInt	outputIndex

std::vector< bitLenInt >	inputIndices

QInterfacePtr	qReg

Constructor & Destructor Documentation

◆ QNeuron() [1/2]

Qrack::QNeuron::QNeuron	(	QInterfacePtr	reg,
		const std::vector< bitLenInt > &	inputIndcs,
		const bitLenInt &	outputIndx
	)

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,
		const real1_f &	alpha
	)

inlinestaticprotected

◆ applyGelu()

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

inlinestaticprotected

◆ applyLeakyRelu()

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

inlinestaticprotected

◆ applyRelu()

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

inlinestaticprotected

◆ clampAngle()

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

inlinestaticprotected

◆ GetInputCount()

bitLenInt Qrack::QNeuron::GetInputCount ( )

inline

◆ GetInputPower()

bitCapIntOcl Qrack::QNeuron::GetInputPower ( )

inline

◆ GetSimulator()

QInterfacePtr Qrack::QNeuron::GetSimulator ( )

inline

Retrieve the simulator.

◆ Learn()

void Qrack::QNeuron::Learn	(	real1_s *	angles,
		const real1_f &	eta,
		const bool &	expected = `true`,
		const bool &	resetInit = `true`,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

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	(	real1_s *	angles,
		const bool &	expected = `true`,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

inline

◆ LearnInternal()

real1_f Qrack::QNeuron::LearnInternal	(	real1_s *	angles,
		const bool &	expected,
		const real1_f &	eta,
		const bitCapIntOcl &	permOcl,
		const real1_f &	startProb,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

inlineprotected

◆ LearnPermutation()

void Qrack::QNeuron::LearnPermutation	(	real1_s *	angles,
		const real1_f &	eta,
		const bool &	expected = `true`,
		const bool &	resetInit = `true`,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

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 ( const real1_f & angle )

inlinestaticprotected

◆ negApplyRelu()

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

inlinestaticprotected

◆ operator=()

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

inline

◆ Predict()

real1_f Qrack::QNeuron::Predict	(	const real1_s *	angles,
		const bool &	expected = `true`,
		const bool &	resetInit = `true`,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

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.

◆ SetIndices()

void Qrack::QNeuron::SetIndices	(	const std::vector< bitLenInt > &	inputIndcs,
		const bitLenInt &	outputIndx
	)

inline

Replace the input and output indices.

◆ SetSimulator()

void Qrack::QNeuron::SetSimulator ( QInterfacePtr sim )

inline

Replace the simulator.

◆ Unpredict()

real1_f Qrack::QNeuron::Unpredict	(	const real1_s *	angles,
		const bool &	expected = `true`,
		const QNeuronActivationFn &	activationFn = `Sigmoid`,
		const real1_f &	alpha = `ONE_R1_F`
	)

inline

"Uncompute" the Predict() method

Member Data Documentation

◆ inputIndices

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

protected

◆ outputIndex

bitLenInt Qrack::QNeuron::outputIndex

protected

◆ qReg

QInterfacePtr Qrack::QNeuron::qReg

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()

◆ GetInputCount()

◆ GetInputPower()

◆ GetSimulator()

◆ Learn()

◆ LearnCycle()

◆ LearnInternal()

◆ LearnPermutation()

◆ negApplyGelu()

◆ negApplyRelu()

◆ operator=()

◆ Predict()

◆ SetIndices()

◆ SetSimulator()

◆ Unpredict()

Member Data Documentation

◆ inputIndices

◆ outputIndex

◆ qReg