Hands-On Quantum Machine Learning with Python/

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Calculating the Posterior Probability

Calculating the Posterior Probability

Learn how we can calculate the posterior probability.

We'll cover the following...

We use the controlled RYR_Y-gate to calculate the posterior probability.

Calculating the posterior probability for a modifier smaller than 1.0

Python 3.5
for step in range(0, len(modifiers)):
    if sorted_modifiers[step] > 1:
        # TO BE IMPLEMENTED
        pass
    else: 
        # apply modifier to the target qubit
        qc.cry(prob_to_angle(1-sorted_modifiers[step]), target, step*2)
        qc.cx(step*2,target)

If the modifier is below 1.01.0 in the code block after line 6, we need to reduce the target probability by a portion of 1−modifier1−modifier in line 8. The target qubit acts as the control qubit. In states when the target qubit is 1, we separate a part of 1−modifier and set the controlled qubit to 1. The controlled qubit has the index step*2. The for step is 0, and the index of this qubit is 0, too. This qubit acts as a trunk. We do not work with it anymore.

The controlled rotation does not change the value of the control qubit. Thus, the target-qubit is 1 in both parts. The following CNOT-gate in ...