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quant-ph2018★ 50 cited
For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances
Fernando G. S. L. Brandao, Michael Broughton, Edward Farhi +2
The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem in…
quant-ph2018
A Universal Training Algorithm for Quantum Deep Learning
Guillaume Verdon, Jason Pye, Michael Broughton
We introduce the Backwards Quantum Propagation of Phase errors (Baqprop) principle, a central theme upon which we construct multiple universal optimization heuristics for training…