Different Strategies for Optimization Using the Quantum Adiabatic Algorithm

Elizabeth Crosson , Edward Farhi , Cedric Yen-Yu Lin , Han-Hsuan Lin , Peter Shor

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classification 🪐 quant-ph

keywords algorithmevolutionquantumadiabaticassignmentinstancesprobabilitystrategies

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We present the results of a numerical study, with 20 qubits, of the performance of the Quantum Adiabatic Algorithm on randomly generated instances of MAX 2-SAT with a unique assignment that maximizes the number of satisfied clauses. The probability of obtaining this assignment at the end of the quantum evolution measures the success of the algorithm. Here we report three strategies which consistently increase the success probability for the hardest instances in our ensemble: decreasing the overall evolution time, initializing the system in excited states, and adding a random local Hamiltonian to the middle of the evolution.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

A Quantum Approximate Optimization Algorithm
quant-ph 2014-11 accept novelty 9.0

A p-layer alternating-operator ansatz on n qubits yields approximation ratios that increase with p, achieving ≥0.6924 for MaxCut on 3-regular graphs at p=1 and approaching 1 in the p→∞ adiabatic limit.