ryanfmandelbaum

## results
shost = 10000
job = execute (qc,Aer.get_backend('qasm_simulator'),shots=shots)
job_result = job.result()
counts = job_result.get_counts(qc)
x = abs(((counts['0']/shots - 0.5)/0.5) * 2 * Z)
Q_Dist = round(m.sqrt(x),4)
print('Quantum Distance: ', round(Q_Dist,3))
print('Euclidean Distance: ',round(Dist,3))

ryanfmandelbaum

# Quantum Circuit
q1 = QuantumRegister(1, name = 'q1')
q2 = QuantumRegister(4, name = 'q2')
c = ClassicalRegister(1, name = 'c')
qc = QuantumCircuit(q1,q2,c)

#States initialization
qc.initialize(phi, q2[0])
qc.initialize(psi, q2[1:4])

ryanfmandelbaum

#The first step is to encode the data into quantum states.
#There are some techniques to do it, in this case Amplitude embedding was used

A = [2,9,8,5]
B = [7,5,10,3]

norm_A = 0 
norm_B = 0
Dist = 0

ryanfmandelbaum

#Import the necessary libraries
import math as m
from qiskit import *
from qiskit import BasicAer
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister

ryanfmandelbaum

#result
shots = 1024
job = execute(qc,Aer.getbackend('qasm_simulator'), shots=shots)
job_result = job.result()
counts = job_result.get_counts(qc)
print(counts)

ryanfmandelbaum

qreg = QuantumRegister(3, 'qreg')
creg = ClassicalRegister(3, 'creg')
qc = QuantumCircuit (qreg,creg)

#Initial state |01>
qc.x(qreg[1])

#swap_test
qc.h(qreg[0]) #apply superposition on the auxiliary qubit
qc.cswap(qreg[0], qreg[1], qreg[2])

ryanfmandelbaum

from qiskit import *
from qiskit import BasicAer
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute

ryanfmandelbaum

from sklearn import metrics
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import ZZFeatureMap
from qiskit.providers.aer import AerSimulator
from qiskit.algorithms.optimizers import SPSA
from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.kernels import QuantumKernel
from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer

ryanfmandelbaum

from sklearn import metrics
from qiskit.circuit.library import ZZFeatureMap
from qiskit.providers.aer import AerSimulator
from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.kernels import QuantumKernel

# Read data and set training and testing sets
input_data = read_data('flash_flood_data.csv')
X_train, y_train, X_test, y_test = train_test_split(input_data)

ryanfmandelbaum

# Initialization
max_trials = 3
qi = QuantumInstance(Aer.get_backend('statevector_simulator'), seed_transpiler=seed, seed_simulator=seed, shots=1024)
rng = np.random.default_rng(seed=seed) # RNG
optim = SLSQP(maxiter=1000) # Init classical optimizer.

def trial():
  # Randomize initial parameters.
  initial_point = (rng.uniform(size=len(qc.parameters)) - 1/2) * np.pi