From what I observed, most hybrid qml architectures surround the ideas of Hamiltonian states, and it seems like our goal to optimize a circuit is to keep energy states as low as possible. But why is that? How is it related to the performance of a model?

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    $\begingroup$ Any optimization problem can be cast as finding the ground state of a Hamiltonian, which happens to be a convenient for quantum systems because Hamiltonians are often physically the thing that can be engineered $\endgroup$ Jan 1 at 21:50
  • $\begingroup$ @QuantumMechanic so how exactly do we cast the "errors" in our ml model as Hamiltonian states? $\endgroup$
    – Ryan Wang
    Jan 1 at 22:30
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    $\begingroup$ My guess is something to do with the energy of a state being a proxy of the "errors" - you take a fixed Hamiltonian to set up the problem, then try to find the state with the lowest energy for that Hamiltonian as a proxy for finding the optimum solution. But I'm not sure, depends on the QML architecture $\endgroup$ Jan 1 at 23:50


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