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On Circuit Depth Scaling For Quantum Approximate Optimization
Focusing on random instances of MAX-2-SAT, we test our predictive model against simulated data with up to 15 qubits.
Training Saturation in Layerwise Quantum Approximate Optimisation
Doing so we discovered that such training always saturates -- called \textit{training saturation} -- at some depth $p^*$, meaning that past a certain depth, overlap can not be improved by adding subsequent layers.
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