Tuning circuits

Finding the right set of parameters, like the right scheduling function in the case of annealers, can be a real challenge. In particular when there is little knowledge about the problem evolution itself.

The adiabatic theorem states that the evolution must be slow enough relative to the relevant energy gap and the rate of change of the Hamiltonian so that transitions from the ground state to higher energy levels are unlikely. A small gap makes this condition more demanding. How can we design the shape of the scheduling function so that it acts in the right places during the evolution?

That takes us into a field that has gone wild: Training Quantum Circuits or using Parameterized Quantum Circuits (PQCs) and optimization techniques to find the right parameter setups. But first we need to generalize beyond what a specific machine can do, this will be our first challenge and the first benefit of going abstract with gate-based computers.