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For QAOA, my understanding is that after we derive our cost Hamiltonian: $H_c|x$>$=C(x)x$ where $C(x)$ is the cost function on input x. We exponentiate it: $e^{-iH_c\theta}$, so we can simulate this Hamiltonian.

While doing so, we have effectively moved the value of the cost function as a phase.

Example

If $C(x)$ is defined as $C(0)=0$, $C(1)=1$ then:

$H=0.5(I-Z)=\begin{bmatrix} 0 & 0\\ 0 & 1 \end{bmatrix}$

We would then get $U_c=e^{-iH_c\theta}=\begin{bmatrix} e^{-i(0)\theta} & 0\\ 0 & e^{-i\theta} \end{bmatrix}$

Now we can see that $U_c|0$>$=e^{-i(0)\theta}|0$> and $U_c|1$>$=e^{-i\theta}|1$>

It looks like all we were trying to do was to add the cost function as a phase for our input. (And also varying that phase by some coefficient $\theta$)

Question

Why are we trying to do this? How does this help us in minimizing the hamiltonian (the cost function)?

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  • $\begingroup$ Do you understand the relationship between the adiabatic theorem and QAOA? Because that’s basically all the justification for QAOA that exists. $\endgroup$ Feb 20, 2022 at 16:48
  • $\begingroup$ @JahanClaes I read about the topic but I wasn't able to grasp the relation. Would you recommend any resources for me? $\endgroup$ Feb 21, 2022 at 7:42
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    $\begingroup$ The original Farhi et al. paper explains their reasoning pretty well $\endgroup$ Feb 21, 2022 at 21:04

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It's not really the phase of the unitary that matters. The hamiltonian is the more important part.

However, the phase that the hamiltonian applys technically isn't useful in of itself, in the sense that it's the connection to Adiabatic Quantum Computing that allows us to choose an ansatz for VQE (which QAOA is just a special case of), which allows for some guarantees such as monotonically increasing accuracy from our classical optimizer as $p$, the number of iterations of the alternating unitaries, increases.

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