from qiskit import *
import numpy as np
%matplotlib inline

from matplotlib import style
style.use('bmh')
style.use('dark_background')

def dj_oracle(case, n):
    # We need to make a QuantumCircuit object to return
    # This circuit has n+1 qubits: the size of the input,
    # plus one output qubit
    oracle_qc = QuantumCircuit(n+1)
    
    # First, let's deal with the case in which oracle is balanced
    if case == "balanced":
        # First generate a random number that tells us which CNOTs to
        # wrap in X-gates:
        b = np.random.randint(1,2**n)
        # Next, format 'b' as a binary string of length 'n', padded with zeros:
        b_str = format(b, '0'+str(n)+'b')
        # Next, we place the first X-gates. Each digit in our binary string 
        # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
        # we apply an X-gate to that qubit:
        for qubit in range(len(b_str)):
            if b_str[qubit] == '1':
                oracle_qc.x(qubit)
        # Do the controlled-NOT gates for each qubit, using the output qubit 
        # as the target:
        for qubit in range(n):
            oracle_qc.cx(qubit, n)
        # Next, place the final X-gates
        for qubit in range(len(b_str)):
            if b_str[qubit] == '1':
                oracle_qc.x(qubit)

    # Case in which oracle is constant
    if case == "constant":
        # First decide what the fixed output of the oracle will be
        # (either always 0 or always 1)
        output = np.random.randint(2)
        if output == 1:
            oracle_qc.x(n)
    
    oracle_gate = oracle_qc.to_gate()
    oracle_gate.name = "Oracle" # To show when we display the circuit
    display(oracle_qc.draw('mpl'))
    return oracle_gate

def dj_algorithm(oracle, n):
    dj_circuit = QuantumCircuit(n+1, n)
    # Set up the output qubit:
    dj_circuit.x(n)
    dj_circuit.h(n)
    # And set up the input register:
    for qubit in range(n):
        dj_circuit.h(qubit)
    # Let's append the oracle gate to our circuit:
    dj_circuit.append(oracle, range(n+1))
    # Finally, perform the H-gates again and measure:
    for qubit in range(n):
        dj_circuit.h(qubit)
    
    for i in range(n):
        dj_circuit.measure(i, i)
    
    return dj_circuit

n = 4
oracle_gate = dj_oracle('constant', n)
dj_circuit = dj_algorithm(oracle_gate, n)
dj_circuit.draw('mpl')



from qiskit.visualization import plot_histogram

backend = Aer.get_backend('qasm_simulator')
results = execute(dj_circuit, backend=backend, shots=1024).result()
answer = results.get_counts()
plot_histogram(answer)

# Load our saved IBMQ accounts and get the least busy backend device with greater than or equal to (n+1) qubits
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_ourense')

# Run our circuit on the least busy backend. Monitor the execution of the job in the queue
from qiskit.tools.monitor import job_monitor

shots = 1024
job = execute(dj_circuit, backend=backend, shots=shots, optimization_level=3)

job_monitor(job)

# Get the results of the computation
results = job.result()
answer = results.get_counts()

plot_histogram(answer)