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import qiskit as qk

# defining our Qubits and Bits
q = qk.QuantumRegister(2)
c = qk.ClassicalRegister(2)

# Creating a circuit with our Qubits and Bits
circuit = qk.QuantumCircuit(q, c)

# Adding Gates

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import qiskit as qk

# Creating Qubits
q = qk.QuantumRegister(2)
# Creating Classical Bits
c = qk.ClassicalRegister(2)

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circuit = qk.QuantumCircuit(q, c)

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# Hadamard Gate on the first Qubit
circuit.h(q[0])
# CNOT Gate on the first and second Qubits
circuit.cx(q[0], q[1])
# Measuring the Qubits
circuit.measure(q, c)

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print (circuit)

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# Using Qiskit Aer's Qasm Simulator
simulator = qk.BasicAer.get_backend('qasm_simulator')

# Simulating the circuit using the simulator to get the result
job = qk.execute(circuit, simulator)
result = job.result()

# Getting the aggregated binary outcomes of the circuit.
counts = result.get_counts(circuit)
print (counts)

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import qiskit import qk

'''
When you run the script once, you do not need to add
your API key again as your account has already been
saved in memory
'''
qk.IBMQ.save_account('YOUR_API_KEY')
qk.IBMQ.load_accounts()

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'''
Applying a Hadamard Gate on the n Qubits
to get a final bitstring of size n 
The bitstring will be converted to a
decimal number (integer) between 0 and 2^3 - 1 (7)
'''
for i in range(n):
    circ.h(q[i])
    
circ.measure(q,c)

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'''
`n` is the number of Qubits needed to 
generate a a random number between
0 and 2^n - 1
'''
n = 3

'''
Creating a Quantum Register with `n` Qubits and 
`n` Classical Bits where n=3

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# The backend simulator available to me
backend = qk.IBMQ.get_backend('ibmqx4')