from qiskit import IBMQ

IBMQ.save_account('API_KEY')

from qiskit import(QuantumCircuit, execute, Aer)

from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter)

import math

import random

# To run as a simulation, replace all 'backend' with 'simulator', and uncomment:

# simulator = Aer.get_backend('qasm_simulator')

qc = QuantumCircuit(5, 5)

qc.h(0)

qc.h(1)

qc.h(2)

qc.h(3)

qc.h(4)

qc.measure([0, 1, 2, 3, 4], [0, 1, 2, 3, 4])

IBMQ.load_account()

provider = IBMQ.get_provider('ibm-q')

backend = provider.get_backend('ibmq_ourense')

# If no mitigation is applied, add ', memory=True', and .get_memory() to job.result()

job = execute(qc, backend, shots=4096, optimization_level=0)

results = job.result()

# Mitigation Measurement, will run twice

cal_circuits, state_labels = complete_meas_cal(qr=qc.qregs[0], circlabel='measurement_calibration')

calculatedJob = execute(cal_circuits, backend, shots=4096, optimization_level=0)

calculatedResults = calculatedJob.result()

meas_fitter = CompleteMeasFitter(calculatedResults, state_labels)

meas_filter = meas_fitter.filter

mitigated_result = meas_filter.apply(results)

device_counts = results.get_counts(qc)

mitigated_counts = mitigated_result.get_counts(qc)

output = []

# Reminder, if mitigated results are disabled, add ".get_memory()" to job.result(), and uncomment:

# for x in range(0, 4096):

# convert = int(mitigated_counts_2[x], 2)

# output.append(convert)

# And comment out:

for x in mitigated_counts:

# Since 'mitigated_counts' returns as a dictionary, we need to round how many times

# it occurs as a probability, then append the key to the output list by that amount.

amount = int(round(mitigated_counts[x], 0))

for y in range(0, amount):

convert = int(x, 2)

output.append(convert)

index = math.floor(random.random() * len(output))

maxDiceRoll = 32

normalizedRoll = (output[index] + 1) / maxDiceRoll

diceSides = 6

result = math.ceil(normalizedRoll * diceSides);

print(result)