The Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to combinatorial optimization problems. Its performance monotonically improves with its depth p. We apply the QAOA to MaxCut on large-girth D-regular graphs. We give an iterative formula to evaluate performance for…

The Quantum Approximate Optimization Algorithm

Source

0
(0)

The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to combinatorial optimization problems. Its performance monotonically improves with its depth p. We apply the QAOA to MaxCut on large-girth D-regular graphs. We give an iterative formula to evaluate performance for any D at any depth p. Looking at random D-regular graphs, at optimal parameters and as D goes to infinity, we find that the p = 11 QAOA beats all classical algorithms (known to the authors) that are free of unproven conjectures. While the iterative formula for these D-regular graphs is derived by looking at a single tree subgraph, we prove that it also gives the ensembleaveraged performance of the QAOA on the Sherrington-Kirkpatrick (SK) model. Our iteration is a compact procedure, but its computational complexity grows as O(p2 4p). This iteration is more efficient than the previous procedure for analyzing QAOA performance on the SK model, and we are able to numerically go to p = 20. Encouraged by our findings, we make the optimistic conjecture that the QAOA, as p goes to infinity, will achieve the Parisi value. We analyze the performance of the quantum algorithm, but one needs to run it on a quantum computer to produce a string with the guaranteed performance.

Speaker: Joao Basso

Watch more Google Quantum AI Talks → https://goo.gle/QuantumAITalks
Subscribe to Google Quantum AI → https://goo.gle/QuantumAI

#QuantumAITalks

Joao Basso; re_ty: Publish;

0 / 5. 0