def decrementExperiment():
"""
This is the main method for the decrement problem
This problem is phrased as follows:
given a set of registers, we will create a prepare statement corresponding to a value in superposition, such as
0x4, the decrement op should output 50% for 0 and 5 correspondingly
We will implement the experiment for 5 qubits (+1 qubit as scratch):
the first qubit is the min value within the circuit initially: x a[0]
(as in the prepareStatement)
So the values in the lookup table are :
h a[1] (2^1) - {'00000': 0.5, '00010': 0.5}
h a[2] (2^2) - {'00000': 0.5, '00100': 0.5}
h a[3] (2^3) - {'00000': 0.5, '01000': 0.5}
h a[4] (2^4) - {'00000': 0.5, '10000': 0.5}
"""
#sets up entries in the lookup table
lookupTable = [
('00000', '00010', "x q[0];\nh q[1];\nrz(0.785398163397448) q[1];\n"),
('00000', '00100', "x q[0];\nh q[2];\nrz(0.785398163397448) q[2];\n"),
('00000', '01000', "x q[0];\nh q[3];\nrz(0.785398163397448) q[3];\n"),
('00000', '10000', "x q[0];\nh q[4];\nrz(0.785398163397448) q[4];\n"),
]
#this array makes it easy to control which experiment is to run by the sim
enabledExperiments = [False, False, True, False]
#default params
params = GAParameters(MProb, CProb, totalEpochs)
incDecExp = IncDecExperiment(registers, lookupTable, r, popSize)
if (enabledExperiments[0]):
# the default roulette wheel selection with a constant CProb, MProb, 80% retention rate
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dec-none")
if (enabledExperiments[1]):
# decreasing CProb, MProb with default roulette wheel selection
try:
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dec-MC")
if (enabledExperiments[2]):
try:
# decreasing CProb, MProb with default roulette wheel selection but delta mutation func
#params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
# MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
#constant CProb, MProb with default roulette wheel selection but delta mutation func
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = False, decreaseC = False, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dec-rw")
if (enabledExperiments[3]):
try:
# decreasing CProb, MProb with default roulette wheel selection but detal mutation func
#params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
# MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
# eliteRatio=eliteRatio)
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = False, decreaseC = False, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
eliteRatio=eliteRatio)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.elitismSelection, \
mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dec-e")
input("Press [enter] to continue.")
def main():
# this is the "prepare" statements used by the maxone problem experiments
hadamardPattern = ""
# places all gates in hadamard
for i in range(0, registers):
hadamardPattern = hadamardPattern + "h q[" + str(i) + "];\n"
#this is the first experiment:
#Roulette wheel selection,
#default multipoint crossover, mutation, and selection function is based on
#the binary string problem and how close the value is to the intended 1111 string value.
#1/(max value - prob of the largest binary string in the quantum experiment) where max value is 2^ num of register - 1 (the max value
#for a binary string represented by the num of registers).
params = GAParameters(MProb,
CProb,
totalEpochs,
openQASMPrepareStatement=hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs,
probCrossover=CProb,
probMutation=MProb,
params=params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="none")
#this is the 2nd experiment:
#Roulette wheel selection,
#default multipoint crossover, mutation, selection function is, again, based on the binary string
#problem, however, the fitness value is weighted based on the following formulation:
#prob1 * value of the first binary string + prob2 * value of the 2nd binary string + ...
params = GAParameters(MProb,
CProb,
totalEpochs,
openQASMPrepareStatement=hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs,
selectionFunc=weightedSelectionFunc,
probCrossover=CProb,
probMutation=MProb,
params=params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="W")
#This is the start of the 3rd experiment with decreasing M and C probabilities
#Default roulette selection method
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
openQASMPrepareStatement = hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs,
probCrossover=CProb,
probMutation=MProb,
params=params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dMC")
#This is the start of the 4th experiment with decreasing M and C probabilities
#weight average selection method
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
openQASMPrepareStatement = hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs,
selectionFunc=weightedSelectionFunc,
probCrossover=CProb,
probMutation=MProb,
params=params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dMCW")
#This is the start of the 5th experiment with decreasing M and C probabilities
#weight average selection method and delta mutation func
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
openQASMPrepareStatement = hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, selectionFunc = weightedSelectionFunc, probCrossover = CProb, \
probMutation= MProb, mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="dMCW")
#test code for elitism
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
eliteRatio=eliteRatio , openQASMPrepareStatement = hadamardPattern)
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, selectionFunc = elitismDefaultSelectionFunc, probCrossover = CProb, \
probMutation= MProb, mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="edMCW")
input("Press [enter] to continue.")
def incrementExperiment():
"""
This is the main method for the increment problem
This problem is phrased as follows:
given a set of registers, we will create a prepare statement corresponding to a value in superposition, such as
4, the increment op should output 50% for 1 and 5 correspondingly
The setup is the same as what's depicted in : https://oreilly-qc.github.io/# (increment & decrement)
but we will use a[5] instead of a[4] and scratch[1] in the preparestatement. Thus, wherever there's a scratch[0],
we will have a[4] instead. If you look at the test code in OpenQASMTest.py, the output should be the same.
We will implement the experiment for 5 qubits (+1 qubit as scratch):
the first qubit is the min value within the circuit: x a[0]
(as in the prepareStatement)
So the values in the lookup table are :
h a[1] (2^1) - {'00010': 0.5, '00100': 0.5}
h a[2] (2^2) - {'00010': 0.5, '00110': 0.5}
h a[3] (2^3) - {'00010': 0.5, '01010': 0.5}
h a[4] (2^4) - {'00010': 0.5, '10010': 0.5}
Note that the lowest value is 00010 = 2 because we wrote a value of 1 in x q[0].
"""
#sets up entries in the lookup table
lookupTable = [
('00010', '00100', "x q[0];\nh q[1];\nrz(0.785398163397448) q[1];\n"),
('00010', '00110', "x q[0];\nh q[2];\nrz(0.785398163397448) q[2];\n"),
('00010', '01010', "x q[0];\nh q[3];\nrz(0.785398163397448) q[3];\n"),
('00010', '10010', "x q[0];\nh q[4];\nrz(0.785398163397448) q[4];\n"),
]
#this array makes it easy to control which experiment is to run by the sim
enabledExperiments = [False, False, False, True]
#default params
params = GAParameters(MProb, CProb, totalEpochs)
incDecExp = IncDecExperiment(registers, lookupTable, r, popSize)
if (enabledExperiments[0]):
# the default roulette wheel selection with a constant CProb, MProb, 80% retention rate
try:
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="inc-none")
if (enabledExperiments[1]):
try:
# decreasing CProb, MProb with default roulette wheel selection
# params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
# MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
#constant CProb, MProb with default roulette wheel selection
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = False, decreaseC = False, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="inc-MC")
if (enabledExperiments[2]):
try:
# decreasing CProb, MProb with default roulette wheel selection but delta mutation func
#params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
# MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
#constant cprob, mprob with default roulette wheel but delta mutation func
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = False, decreaseC = False, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.rouletteWheelSelection, \
mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="inc-rw")
if (enabledExperiments[3]):
try:
# decreasing CProb, MProb with elitism selection but delta mutation func
# params = GAParameters(MProb, CProb, totalEpochs, decreaseM = True, decreaseC = True, \
# MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
# eliteRatio=eliteRatio)
# constant CProb, MProb with elitism selection but delta mutation func
params = GAParameters(MProb, CProb, totalEpochs, decreaseM = False, decreaseC = False, \
MStartEpoch = mStartEpoch, MEndEpoch = mEndEpoch, CStartEpoch = cStartEpoch, CEndEpoch = cEndEpoch, \
eliteRatio=eliteRatio)
ga = Ga(r,
popSize,
registers,
generationFunction=QASMPrimitiveInstruction,
minSize=minSize,
maxSize=maxSize)
stats = ga.evolve(totalEpochs, probCrossover = CProb, probMutation= MProb, \
evaluationFunc = incDecExp.evaluationFunction, selectionFunc= incDecExp.elitismSelection, \
mutationArgFunc = deltaMutationFunc, params = params)
except Exception:
traceback.print_exc()
generateSummaries(stats=stats, experimentName="inc-e")
input("Press [enter] to continue.")