Qiskit Programming - QML Cheat Sheet (DRAFT) by Dinesh1102

#Define Simulator
S_simulator = Aer.backends(name='statevector_simulator')[0]
M_simulator = Aer.backends(name='qasm_simulator')[0]

#Define Register (1 qubit)
qreg_q = QuantumRegister(1,'q')
creg_c = ClassicalRegister(1,'c')

#Define quantum circuit
qc = QuantumCircuit(qreg_q,creg_c)

#Add Quantum gates to Circuit
qc.h(qreg_q[0])
# Add Measure
qc.measure(qreg_q,creg_c)

#RUn circuit on Simulator
job = execute(qc,M_simulator)
result = job.result()
result.get_counts(qc)

Names	Example	Notes
I, Identity	qc.id(0) or qc.i(o)	Applies I gate to qubit 0.
H, Hadamard	qc.h(0)	Applies H gate to qubit 0.
X	qc.x(0)	Applies X gate to qubit 0.
Y	qc.y([0,1,2])	Applies Y gates to qubits 0, 1, and 2.
Z	qc.z(0)	Applies Z gate to qubit 0. Equivalent to P gate with π phase rotation.
P, Phase	qc.p(math.pi/2,0)	Applies P gate with π/2 phase rotation to qubit 0.
S	qc.s(0)	Applies S gate to qubit 0. Equivalent to P gate with π/2 phase rotation.
S†	qc.sdg(0)	Applies S† gate to qubit 0. Equivalent to P gate with 3π/2 phase rotation.
SX	qc.sx(0)	Applies SX (square root of X) gate to qubit 0. Equivalent to RX gate with π/2 rotation.
T	qc.t(0)	Applies T gate to qubit 0. Equivalent to P gate with π/4 phase rotation.
T†	qc.tdg(0)	Applies T† gate to qubit 0. Equivalent to P gate with 7π/4 phase rotation.
RX	qc.rx(math.pi/4,0)	Applies RX gate with π/4 rotation to qubit 0.
RY	qc.ry(math.pi/8,0)	Applies RY gate with π/8 rotation to qubit 0.
RZ	qc.rz(math.pi/2,0)	Applies RZ gate with π/2 rotation to qubit 0.
U	qc.u(math.pi/2,0,math.pi,5)	Applies rotation with 3 Euler angles to qubit 5.

from qiskit import *
from qiskit.tools.jupyter import *
from qiskit.visualization import *
import matplotlib.pyplot as plotter
import numpy as np
from IPython.display import display, Math, Latex
%matplotlib inline

sim = Aer.get_backend('aer_simulator')

qr = QuantumRegister(3)
crz = ClassicalRegister(1)
crx = ClassicalRegister(2)            # we will need seperates registers for using 'c_if' later.
qc = QuantumCircuit(qr,crz,crx)

qc.x(0)
qc.h(0)                               # 'psi' can't be unknown to us as we are creating it here. Let us take '-' state as our 'psi'. This is done by operating X and H gate on the q0 i.e., H.X|0>
                                      # We will verify later if the '-' is been teleported.
qc.draw('mpl')


qc.h(1)
qc.cx(1,2)                            # creating a bell state
qc.barrier()                          # Use barrier to separate steps, everything till this barrier is just intialisation.
qc.draw('mpl')

qc.cx(0,1) # '0' and '1' are with Alice and '2' is with Bob.
# psi_1 prepared.
qc.barrier()                          # Use barrier to separate steps
qc.draw('mpl')

qc.h(0)
# psi_2 prepared.
qc.barrier()
qc.draw('mpl')

qc.measure(0,0)
qc.measure(1,1)
qc.barrier()
qc.draw('mpl')

qc.x(2).c_if(crx,1)  # 'c_if' compares a classical register with a value (either 0 or 1) and performs the 
qc.z(2).c_if(crz,1)  # operation if they are equal.
qc.draw('mpl')

qc.h(2)
qc.measure(2,crx[1])
qc.draw('mpl')

qobj = assemble(qc)
result = sim.run(qobj).result()
counts = result.get_counts()
plot_histogram(counts)

from qiskit import *
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.visualization import *
import matplotlib.pyplot as plotter
import numpy as np
from IPython.display import display, Math, Latex
import math as m

%matplotlib inline

from qiskit import *
from qiskit.visualization import plot_histogram
%config InlineBackend.figure_format = 'svg'

qc_ab = QuantumCircuit(6,6) #Create a quantum circuit with 6 qubits and 6 classical bits


##ENCODE BIT STRING
#The random bit sequence Alice needs to encode is: 100100, so the first and fourth qubits are flipped from |0> -> |1>
qc_ab.x(0) #The first qubit is indexed at 0, following Python being zero-indexed. From now on it'll be referred to as qubit 0 and so on.
qc_ab.x(3) 
qc_ab.barrier()


##ALICE CHOOSES
#Alice randomly chooses to apply an X or an H.  
#Note that since the state is already either a |0> or |1>, a Z essentially leaves the qubit state unchanged. But let's write it anyway, shall we? 

qc_ab.h(0) # or qc.z(0) # switch these based on your own choice
qc_ab.z(1) # or qc.h(1)
qc_ab.z(2) # or qc.h(2)
qc_ab.h(3) # or qc.z(3)
qc_ab.z(4) # or qc.h(4)
qc_ab.h(5) # or qc.z(5)
qc_ab.barrier()


##BOB CHOOSES
#Alice sends the qubit sequence to Bob, and Bob randomly chooses measurements
qc_ab.h(0) # or qc.z(0) # switch these based on your own choice
qc_ab.z(1) # or qc.h(1)
qc_ab.h(2) # or qc.z(2)
qc_ab.h(3) # or qc.z(3)
qc_ab.z(4) # or qc.h(4)
qc_ab.z(5) # or qc.h(5)
qc_ab.barrier()


##PUBLICIZE CHOICES
#Alice and Bob publicize their choices and only retain those for which their choices match. In this case: qubits 0,1,3,4. 
#Note: technically Bob performs the measurement BEFORE publicizing, but we're combining the two here since no one is actually communicating.

qc_ab.measure(0,0)
qc_ab.measure(1,1)
qc_ab.measure(3,3)
qc_ab.measure(4,4)


#qc_ab.measure(2,2) #come back to uncomment these to see what happens to the results after you've run this once
#qc_ab.measure(5,5)


qc_ab.draw(output='mpl') #let's see what this circuit looks like!

##EXECUTE
result = execute(qc_ab, Aer.get_backend('qasm_simulator')).result().get_counts() #We're only making use of the simulator. Refer to [2] to see how you can run this on a real quantum computer.
plot_histogram(result)

#Same situation but now with an eavesdropper (Eve) 

qc_aeb = QuantumCircuit(6,6) #Create a quantum circuit with 6 qubits and 6 classical bits

##ENCODE BIT STRING
qc_aeb.x(0) 
qc_aeb.x(3) 
qc_aeb.barrier()

##ALICE CHOOSES
qc_aeb.h(0) 
qc_aeb.z(1) 
qc_aeb.z(2) 
qc_aeb.h(3) 
qc_aeb.z(4) 
qc_aeb.h(5) 
qc_aeb.barrier()

##EVE CHOOSES
qc_aeb.h(0) #play around with these to see how many states with non-zero probabilities show up at the end for a fixed set of Alice's and Bob's choices
qc_aeb.z(1) 
qc_aeb.h(2) 
qc_aeb.h(3) 
qc_aeb.z(4) 
qc_aeb.z(5) 
qc_aeb.barrier()

##BOB CHOOSES
qc_aeb.h(0) 
qc_aeb.z(1) 
qc_aeb.h(2) 
qc_aeb.h(3) 
qc_aeb.z(4) 
qc_aeb.z(5) 
qc_aeb.barrier()


##PUBLICIZE CHOICES

qc_aeb.measure(0,0)
qc_aeb.measure(1,1)
qc_aeb.measure(3,3)
qc_aeb.measure(4,4)

#qc_aeb.measure(2,2) #come back to uncomment these to see what happens to the results after you've run this once
#qc_aeb.measure(5,5)

qc_aeb.draw(output='mpl') #let's see what this circuit looks like!

##EXECUTE
result = execute(qc_aeb, Aer.get_backend('qasm_simulator')).result().get_counts()
plot_histogram(result)