parallelQAE

Parallel Quantum Amplitude Estimation¶

The functions in this notebook are based on the following paper:

Error Resilient Quantum Amplitude Estimation from Parallel Quantum Phase Estimation

We run a standard serial Quantum Amplitude Estimation (QAE) with two different initializations in order to show the effect of the approximation of the eigenstates. Furthermore, we compare the result of a standard QAE and a parallel implementation. Finally, we draw the circuits for three different implementations of QAE with a precision of two qubits.

In [1]:

import pygrnd
from pygrnd.qc.parallelQAE import *

from qiskit.visualization import plot_histogram
import matplotlib as mpl
from matplotlib import style
mpl.style.use('seaborn')

Definition of a model¶

This quantum circuit corresponds to a risk model, see e.g. A Quantum Algorithm for the Sensitivity Analysis of Business Risks
We want to find the probabilities of certain results by QAE.

In [2]:

#
# A simple example on 4 qubits.
#

p0=3.765510036975144
p1=3.1948146171818914
p2=4.824329716208535
p3=1.7459012356232146
p4=2.527885232509252

qr=QuantumRegister(4,'q')
qc=QuantumCircuit(qr)
qc.u(p0,0,0,qr[3])
qc.append(XGate().control(ctrl_state='0'),[qr[3],qr[2]])
qc.append(UGate(p1,0,0).control(ctrl_state='0'),[qr[2],qr[1]])
qc.append(UGate(p2,0,0).control(ctrl_state='1'),[qr[2],qr[1]])
qc.append(UGate(p3,0,0).control(ctrl_state='0'),[qr[1],qr[0]])
qc.append(UGate(p4,0,0).control(ctrl_state='1'),[qr[1],qr[0]])
modelGate=qc.to_gate()
modelGate.label="model"
qc.draw(output='mpl')

Out[2]:

In [3]:

#
# Plot the probabilities of the results. This is just for the visualization of
# the properties of the model circuit.
#

qr=QuantumRegister(4,'q')
cr=ClassicalRegister(4,'c')
qc=QuantumCircuit(qr,cr)
qc.append(modelGate,qr)
qc.measure(qr,cr)

backend = Aer.get_backend('statevector_simulator')
job = execute(qc, backend,shots=10000)
result=job.result()
plot_histogram(result.get_counts())

Out[3]:

Standard QAE - exact eigenvectors vs. eigenstate approximation¶

Compare standard QAE with two different initializations for an output precision of $6$ bit
We consider the good state '1010', the result should be close to 0.08
Case 1: Use the model as initialization; this leads to a superposition of two eigenvectors.
Case 2: Use the approximation of one eigenvector

In [4]:

goodState='1010'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(modelGate, [goodState])

# Construct the standard version of QAE.
qcStandard=circuitStandardQAE(modelGate, grover, 6)

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(modelGate, goodState)

# Construct the standard version of QAE, where the initialization is replaced by the approximation.
qcApprox=circuitStandardQAE(ep, grover, 6)

In [5]:

#
# Run simulations for both circuits.
#

backend = Aer.get_backend('qasm_simulator')

job = execute(qcStandard, backend,shots=10000)
countsStandard=job.result().get_counts()

job = execute(qcApprox, backend,shots=10000)
countsApprox=job.result().get_counts()

In [6]:

#
# We see that the standard version of QAE uses a uniform superposition of two eigenvectors and this 
# leads to two results, which are equivalent.
#
# In contrast to this, the approximation of the eigenvector leads to just one of the two results.
#

plot_histogram([countsStandard,countsApprox],legend=['Standard','Approx'],figsize=(20,10))

Out[6]:

In [7]:

#
# We can map the binary strings of the measured results to the corresponding probability
# values. We also add up the probabilities of equivalent results.
#

probsStandard=getHistogramProbabilityValues(countsStandard)
probsApprox=getHistogramProbabilityValues(countsApprox)
plot_histogram([probsStandard,probsApprox],legend=['Standard','Approx'],figsize=(20,10))

Out[7]:

Standard QAE vs. parallel QAE with intermediate resets¶

We compare the standard version of QAE with standard initialization with the parallel QAE with an approximation of an eigenstate.
We consider the good state '1010'.
In order to make simulations possible, we use the version with intermediate resets of the quantum register.

In [8]:

goodState='1010'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(modelGate, [goodState])

# Construct the standard version of QAE.
qcStandard=circuitStandardQAE(modelGate, grover, 6)

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(modelGate, goodState)

# Parallel QAE with an approximation of an eigenvector.
qcParallel=circuitStandardParallelQAEwithResets(ep, grover, 6)

In [9]:

#
# Run simulations for both circuits.
#

backend = Aer.get_backend('qasm_simulator')

job = execute(qcStandard, backend, shots=10000)
countsStandard=job.result().get_counts()

job = execute(qcParallel, backend, shots=10000)
countsParallel=job.result().get_counts()

In [10]:

#
# The binary results of QAE are mapped to the corresponding probability values. 
# We see that the approximation of the eigenvector leads to a lower fidelity
# compared to the standard version of QAE, which cannot be run in parallel.
#

probsStandard=getHistogramProbabilityValues(countsStandard)
probsParallel=getHistogramProbabilityValues(countsParallel)
plot_histogram([probsStandard,probsParallel],legend=['Standard','Parallel'],figsize=(20,10))

Out[10]:

Comparison of circuit structures¶

We plot the circuits for a precision of $2$ bits for three different cases
(1) Standard QAE
(2) Parallel QAE with approximation of eigenvector
(3) Parallel QAE with approximation of eigenvector and intermediate resets

In [11]:

#
# The standard QAE.
#

goodState='1010'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(modelGate, [goodState])

# Construct the standard version of QAE.
qcStandard=circuitStandardQAE(modelGate, grover, 2)

qcStandard.draw(output='mpl')

Out[11]:

In [12]:

#
# The parallel QAE with an approximation of an eigenvector.
#

goodState='1010'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(modelGate, [goodState])

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(modelGate, goodState)

# Parallel QAE with an approximation of an eigenvector.
qcParallel=circuitStandardParallelQAE(ep, grover, 2)

qcParallel.draw(output='mpl')

Out[12]:

In [13]:

#
# The parallel QAE with an approximation of an eigenvector and intermediate resets.
#

goodState='1010'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(modelGate, [goodState])

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(modelGate, goodState)

# Parallel QAE with an approximation of an eigenvector with intermediate resets.
qcParallelResets=circuitStandardParallelQAEwithResets(ep, grover, 2)

qcParallelResets.draw(output='mpl')

Out[13]:

Testing with different risk model built with pygrnd function¶

In [14]:

from pygrnd.qc.helper import *
from pygrnd.qc.brm import *
from pygrnd.qc.brm_oracle import *
from pygrnd.qc.QAE import *

In [15]:

## small example

RI1=0.8
RI2=0.2
RI3=0.1
RI4=0.05
T23=0.2
T24=0.1

nodes=['0','1','2','3'] # risk items defition
edges=[('1','2'),('1','3')] # correlations

probsNodes={'0':RI1,'1':RI2,'2':RI3,'3':RI4} # intrinsic probs
probsEdges={('1','2'):T23,('1','3'):T24} # transition probs

print("Number of risk items = ",len(nodes))
print("Number of correlations = ",len(edges))

rm, mat = brm(nodes, edges, probsNodes, probsEdges, model2gate=True)
#rm.draw(output='mpl')

Number of risk items =  4
Number of correlations =  2

In [16]:

qr=QuantumRegister(len(nodes),'qr')
qc=QuantumCircuit(qr)
qc.append(rm,qr)
qc.measure_all()
display(qc.draw(output='mpl'))

backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend,shots=100000)
#result=job.result()
counts=job.result().get_counts()
plot_histogram(counts,figsize=(16,8))

Out[16]:

Serial QAE¶

In [17]:

goodState='1111'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(rm, [goodState])

# Construct the standard version of QAE.
qcStandard=circuitStandardQAE(rm, grover, 6)

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(rm, goodState)

# Construct the standard version of QAE, where the initialization is replaced by the approximation.
qcApprox=circuitStandardQAE(ep, grover, 6)

In [18]:

#
# Run simulations for both circuits.
#

backend = Aer.get_backend('qasm_simulator')

job = execute(qcStandard, backend,shots=10000)
countsStandard=job.result().get_counts()

job = execute(qcApprox, backend,shots=10000)
countsApprox=job.result().get_counts()

In [19]:

#
# We can map the binary strings of the measured results to the corresponding probability
# values. We also add up the probabilities of equivalent results.
#

probsStandard=getHistogramProbabilityValues(countsStandard)
probsApprox=getHistogramProbabilityValues(countsApprox)
plot_histogram([probsStandard,probsApprox],legend=['Standard','Approx'],figsize=(20,10))

Out[19]:

Parallel QAE¶

In [20]:

goodState='1111'

# Construct the Grover operator for the model from above.
grover=constructGroverOperator(rm, [goodState])

# Construct the standard version of QAE.
qcStandard=circuitStandardQAE(rm, grover, 7)

# Construct the approximation of one eigenvector of the Grover operator.
ep=generateStateApproximation(rm, goodState)

# Parallel QAE with an approximation of an eigenvector.
qcParallel=circuitStandardParallelQAEwithResets(ep, grover, 7)

In [21]:

#
# Run simulations for both circuits.
#

backend = Aer.get_backend('qasm_simulator')

job = execute(qcStandard, backend, shots=10000)
countsStandard=job.result().get_counts()

job = execute(qcParallel, backend, shots=10000)
countsParallel=job.result().get_counts()

In [22]:

#
# The binary results of QAE are mapped to the corresponding probability values. 
# We see that the approximation of the eigenvector leads to a lower fidelity
# compared to the standard version of QAE, which cannot be run in parallel.
#

probsStandard=getHistogramProbabilityValues(countsStandard)
probsParallel=getHistogramProbabilityValues(countsParallel)
plot_histogram([probsStandard,probsParallel],legend=['Standard','Parallel'],figsize=(20,10))

Out[22]: