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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.
Probing Criticality in Quantum Spin Chains with Neural Networks
The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck.
Reachability Deficits in Quantum Approximate Optimization
The quantum approximate optimization algorithm (QAOA) has rapidly become a cornerstone of contemporary quantum algorithm development.
Computational Phase Transition Signature in Gibbs Sampling
This so called, algorithmic or computational phase transition signature, has yet-to-be observed in contemporary physics based processors.
