This algorithm can search the desired choice among unorganized database.

I'm going to talk about the case that I want to choose .

The first thing you have to do is to put hadamard gates on all the qubits. In this repository, I prepared 2 qubits. So, I got 4 choices.

After you create the superposition, you have to put something called the phase oracle.
The following codes is those of the phase oracle for .

def oracle0(qci,n,q1,q2):

    for i in range(n):    
    
      qci.s(q[i])     
      
    qci.cz(q[q1],q[q2]) 
    
    for i in range(n):  
    
        qci.s(q[i])     
         

In order to make sure that the phase of , you have to write the following code after you measure the quantum states.

backend = BasicAer.get_backend('statevector_simulator')  

job = execute(qc,backend)  

result = job.result()  

outputstate = result.get_statevector(qc, decimals=3)  

print(outputstate)  

The outcome would be [ -0.5+0.j 0.5+0.j 0.5+0.j 0.5+0.j]

First, the probability of getting each states are 50% and the one for the desired choice is just flipped, which is -50%.

Then, I substract it from the average of all the probability((50+50+50-50)/4 = 25%), so the outcome would be 75%.
After that, I should add that value to the average again, which would end up to be 100%.

You can realize this by putting the following codes.

#bn is the number of qubits, which is 2 in this case.   
for j in range(bn):   
    qc.h(q[j])   
    qc.x(q[j])  
oracle0(qc,2,0,1)  
for k in range(bn):  
    qc.x(q[k])  
    qc.h(q[k])  

This is the result on the QASM simulator.

And this is the result on the real device(ibmq_20_tokyo).

You can find the cases to maximize not only ,but also , and . So,feel free to try them on qiskit.