qneuron.hpp

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//
// (C) Daniel Strano and the Qrack contributors 2017-2023. All rights reserved.
//
// This is a multithreaded, universal quantum register simulation, allowing
// (nonphysical) register cloning and direct measurement of probability and
// phase, to leverage what advantages classical emulation of qubits can have.
//
// Licensed under the GNU Lesser General Public License V3.
// See LICENSE.md in the project root or https://www.gnu.org/licenses/lgpl-3.0.en.html
// for details.
 
#pragma once
 
#include "common/qneuron_activation_function.hpp"
#include "qinterface.hpp"
 
#include <algorithm>
 
namespace Qrack {
 
class QNeuron;
typedef std::shared_ptr<QNeuron> QNeuronPtr;
 
class QNeuron {
protected:
    bitLenInt outputIndex;
    std::vector<bitLenInt> inputIndices;
    QInterfacePtr qReg;
 
    static real1_f applyRelu(const real1_f& angle) { return std::max((real1_f)ZERO_R1_F, (real1_f)angle); }
 
    static real1_f negApplyRelu(const real1_f& angle) { return -std::max((real1_f)ZERO_R1_F, (real1_f)angle); }
 
    static real1_f applyGelu(const real1_f& angle) { return angle * (1 + erf((real1_s)(angle * SQRT1_2_R1))); }
 
    static real1_f negApplyGelu(const real1_f& angle) { return -angle * (1 + erf((real1_s)(angle * SQRT1_2_R1))); }
 
    static real1_f applyAlpha(real1_f angle, const real1_f& alpha)
    {
        real1_f toRet = ZERO_R1;
        if (angle > PI_R1) {
            angle -= PI_R1;
            toRet = PI_R1;
        } else if (angle <= -PI_R1) {
            angle += PI_R1;
            toRet = -PI_R1;
        }
 
        return toRet + (pow((2 * abs(angle) / PI_R1), alpha) * (PI_R1 / 2) * ((angle < 0) ? -1 : 1));
    }
 
    static real1_f applyLeakyRelu(const real1_f& angle, const real1_f& alpha) { return std::max(alpha * angle, angle); }
 
    static real1_f clampAngle(real1_f angle)
    {
        // From Tiama, (OpenAI ChatGPT instance)
        QRACK_CONST real1_f PI_2 = 2 * PI_R1;
        QRACK_CONST real1_f PI_4 = 4 * PI_R1;
        angle = fmod(angle, PI_4);
        if (angle <= -PI_2) {
            angle += PI_4;
        } else if (angle > PI_2) {
            angle -= PI_4;
        }
 
        return angle;
    }
 
public:
    QNeuron(QInterfacePtr reg, const std::vector<bitLenInt>& inputIndcs, const bitLenInt& outputIndx)
        : outputIndex(outputIndx)
        , inputIndices(inputIndcs)
        , qReg(reg)
    {
    }
 
    QNeuron(const QNeuron& toCopy)
        : QNeuron(toCopy.qReg, toCopy.inputIndices, toCopy.outputIndex)
    {
    }
 
    QNeuron& operator=(QNeuron& toCopy)
    {
        qReg = toCopy.qReg;
        inputIndices = toCopy.inputIndices;
        outputIndex = toCopy.outputIndex;
 
        return *this;
    }
 
    void SetSimulator(QInterfacePtr sim) { qReg = sim; }
 
    void SetIndices(const std::vector<bitLenInt>& inputIndcs, const bitLenInt& outputIndx)
    {
        inputIndices = inputIndcs;
        outputIndex = outputIndx;
    }
 
    QInterfacePtr GetSimulator() { return qReg; }
 
    bitLenInt GetInputCount() { return inputIndices.size(); }
 
    bitCapIntOcl GetInputPower() { return pow2Ocl(inputIndices.size()); }
 
    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)
    {
        if (resetInit) {
            qReg->SetBit(outputIndex, false);
            qReg->RY((real1_f)(PI_R1 / 2), outputIndex);
        }
 
        if (inputIndices.empty()) {
            // If there are no controls, this "neuron" is actually just a bias.
            switch (activationFn) {
            case ReLU:
                qReg->RY((real1_f)(applyRelu(angles[0U])), outputIndex);
                break;
            case GeLU:
                qReg->RY((real1_f)(applyGelu(angles[0U])), outputIndex);
                break;
            case Generalized_Logistic:
                qReg->RY((real1_f)(applyAlpha(angles[0U], alpha)), outputIndex);
                break;
            case Leaky_ReLU:
                qReg->RY((real1_f)(applyLeakyRelu(angles[0U], alpha)), outputIndex);
                break;
            case Sigmoid:
            default:
                qReg->RY((real1_f)(angles[0U]), outputIndex);
            }
        } else if (activationFn == Sigmoid) {
#if (FPPOW < 5) || (FPPOW > 6)
            const bitCapIntOcl p = GetInputPower();
            std::unique_ptr<real1[]> _angles(new real1[p]);
            std::copy(angles, angles + p, _angles.get());
            qReg->UniformlyControlledRY(inputIndices, outputIndex, _angles.get());
#else
            qReg->UniformlyControlledRY(inputIndices, outputIndex, angles);
#endif
        } else {
            const bitCapIntOcl inputPower = GetInputPower();
            std::unique_ptr<real1[]> nAngles(new real1[inputPower]);
            switch (activationFn) {
            case ReLU:
                std::transform(angles, angles + inputPower, nAngles.get(), applyRelu);
                break;
            case GeLU:
                std::transform(angles, angles + inputPower, nAngles.get(), applyGelu);
                break;
            case Generalized_Logistic:
                std::transform(
                    angles, angles + inputPower, nAngles.get(), [&alpha](real1_s a) { return applyAlpha(a, alpha); });
                break;
            case Leaky_ReLU:
                std::transform(
                    angles, angles + inputPower, nAngles.get(), [&alpha](real1_s a) { return applyLeakyRelu(a, alpha); });
                break;
            case Sigmoid:
            default:
                break;
            }
            qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
        }
        real1_f prob = qReg->Prob(outputIndex);
        if (!expected) {
            prob = ONE_R1_F - prob;
        }
        return prob;
    }
 
    real1_f Unpredict(const real1_s* angles, const bool& expected = true,
        const QNeuronActivationFn& activationFn = Sigmoid, const real1_f& alpha = ONE_R1_F)
    {
        if (inputIndices.empty()) {
            // If there are no controls, this "neuron" is actually just a bias.
            switch (activationFn) {
            case ReLU:
                qReg->RY((real1_f)(negApplyRelu(angles[0U])), outputIndex);
                break;
            case GeLU:
                qReg->RY((real1_f)(negApplyGelu(angles[0U])), outputIndex);
                break;
            case Generalized_Logistic:
                qReg->RY((real1_f)(-applyAlpha(angles[0U], alpha)), outputIndex);
                break;
            case Leaky_ReLU:
                qReg->RY((real1_f)(-applyLeakyRelu(angles[0U], alpha)), outputIndex);
                break;
            case Sigmoid:
            default:
                qReg->RY((real1_f)(-angles[0U]), outputIndex);
            }
        } else {
            const bitCapIntOcl inputPower = GetInputPower();
            std::unique_ptr<real1[]> nAngles(new real1[inputPower]);
            switch (activationFn) {
            case ReLU:
                std::transform(angles, angles + inputPower, nAngles.get(), negApplyRelu);
                qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
                break;
            case GeLU:
                std::transform(angles, angles + inputPower, nAngles.get(), negApplyGelu);
                qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
                break;
            case Generalized_Logistic:
                std::transform(
                    angles, angles + inputPower, nAngles.get(), [&alpha](real1_s a) { return -applyAlpha(a, alpha); });
                qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
                break;
            case Leaky_ReLU:
                std::transform(angles, angles + inputPower, nAngles.get(),
                    [&alpha](real1_s a) { return -applyLeakyRelu(a, alpha); });
                qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
                break;
            case Sigmoid:
            default:
                std::transform(angles, angles + inputPower, nAngles.get(), [](real1_s a) { return -a; });
                qReg->UniformlyControlledRY(inputIndices, outputIndex, nAngles.get());
            }
        }
        real1_f prob = qReg->Prob(outputIndex);
        if (!expected) {
            prob = ONE_R1_F - prob;
        }
        return prob;
    }
 
    real1_f LearnCycle(real1_s* angles, const bool& expected = true, const QNeuronActivationFn& activationFn = Sigmoid,
        const real1_f& alpha = ONE_R1_F)
    {
        const real1_f result = Predict(angles, expected, false, activationFn, alpha);
        Unpredict(angles, expected, activationFn, alpha);
        return result;
    }
 
    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)
    {
        real1_f startProb = Predict(angles, expected, resetInit, activationFn, alpha);
        Unpredict(angles, expected, activationFn, alpha);
        if ((ONE_R1 - startProb) <= FP_NORM_EPSILON) {
            return;
        }
        const bitCapIntOcl inputPower = GetInputPower();
        for (bitCapIntOcl perm = 0U; perm < inputPower; ++perm) {
            startProb = LearnInternal(angles, expected, eta, perm, startProb, activationFn, alpha);
            if (0 > startProb) {
                break;
            }
        }
    }
 
    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)
    {
        const real1_f startProb = Predict(angles, expected, resetInit, activationFn, alpha);
        Unpredict(angles, expected, activationFn, alpha);
        if ((ONE_R1 - startProb) <= FP_NORM_EPSILON) {
            return;
        }
        bitCapIntOcl perm = 0U;
        for (size_t i = 0U; i < inputIndices.size(); ++i) {
            if (qReg->M(inputIndices[i])) {
                perm |= pow2Ocl(i);
            }
        }
 
        LearnInternal(angles, expected, eta, perm, startProb);
    }
 
protected:
    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)
    {
        const real1_s origAngle = angles[permOcl];
        real1_s& angle = angles[permOcl];
 
        const real1_s delta = (real1_s)(eta * PI_R1);
 
        // Try positive angle increment:
        angle += delta;
        const real1_f plusProb = LearnCycle(angles, expected, activationFn, alpha);
 
        // If positive angle increment is not an improvement,
        // try negative angle increment:
        angle = origAngle - delta;
        const real1_f minusProb = LearnCycle(angles, expected, activationFn, alpha);
 
        if ((startProb >= plusProb) && (startProb >= minusProb)) {
            // If neither increment is an improvement,
            // restore the original variational parameter.
            angle = origAngle;
            return startProb;
        }
 
        if (plusProb > minusProb) {
            angle = origAngle + delta;
            return plusProb;
        }
 
        return minusProb;
    }
};
} // namespace Qrack