# Add one entanglement not to have two adjacent input
bdr.cz(0, 1)
# The block repeats with the other parameters
block(bdr, x, params[4:])
return bdr
##############################
# We instantiate and train the classifier
nbqbits = 2
nbparams = 8
qc = mqCircuitML(make_circuit=irisCircuit, nbqbits=nbqbits, nbparams=nbparams)
bitstr = ['00', '01', '10']
model = Classifier(qc, bitstr).fit(input_train, target_train, method="BFGS")
##############################
# We test it using qiskit
backend = Backends("ibmq_ourense", hub="ibm-q")
qc = qkCircuitML(
make_circuit=irisCircuit,
nbqbits=nbqbits,
nbparams=nbparams,
backend=backend,
bdr.cz(0,1).allin(x[[4,5]])
bdr.cz(0,1).allin(p[[4,5]])
bdr.cz(0,1).allin(x[[6,7]])
bdr.cz(0,1).allin(p[[6,7]])
bdr.cz(0,1).allin(x[[8,9]])
bdr.cz(0,1).allin(p[[8,9]])
bdr.cz(0,1).input(0, x[10])
bdr.cz(0,1).input(0, p[10])
def wineCircuit(bdr, x, params):
block(bdr, x, params[:11])
bdr.cz(0,1)
block(bdr, x, params[11:])
return bdr
input_train = np.random.rand(2940,11)
target_train = (3*np.random.rand((2940))).astype(int)
qc = mqCircuitML(make_circuit=wineCircuit,
nbqbits=nbqbits, nbparams=nbparams)
model = Classifier(qc, bitstr)
model.fit(input_train, target_train)
def simple_circuit(bdr, x, params):
bdr.allin(x).cz(0, 1).allin(params[:2])
bdr.cz(0, 1).allin(params[2:4])
return bdr
##############################
# Now we instantiate the circuit
nbqbits = 2
nbparams = 4
qc = mqCircuitML(make_circuit=simple_circuit,
nbqbits=nbqbits,
nbparams=nbparams)
bitstr = ['00', '11']
# We can use exact probabilities
model = Classifier(qc, bitstr)
model.fit(X, y, save_loss_progress=True)
# Or pass though shots-based estimates
model2 = Classifier(qc, bitstr, nbshots=300)
model2.fit(X, y, method='COBYLA', save_loss_progress=True)
if FIGURES:
fig, ax = plt.subplots(figsize=(5, 3))
fig.set_tight_layout(True)
if not isdir("figures-ex"):
from os import mkdir
mkdir("figures-ex")
#############################################
from polyadicqml.manyq import mqCircuitML
import numpy as np
# `make_circuit` can have any name
def simple_circuit(bdr, x, params):
return bdr.allin(x).cz(0,1).allin(params)
# We instantiate the circuitML
from polyadicqml.manyq import mqCircuitML
circuit = mqCircuitML(simple_circuit, nbqbits=2, nbparams=2)
# We create an input matrix and a param vector
import numpy as np
X = np.array([[-np.pi/4, np.pi/4],
[np.pi/4, -np.pi/4]])
params = np.array([np.pi/2, -np.pi/2])
probs = circuit.run(X, params)
counts = circuit.run(X, params, nbshots=100)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()