TY - JOUR
T1 - Different Strategies for Optimization Using the Quantum Adiabatic Algorithm
Y1 - 2014
A1 - Elizabeth Crosson
A1 - Edward Farhi
A1 - Cedric Yen-Yu Lin
A1 - Han-Hsuan Lin
A1 - Peter Shor
AB - 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.
UR - http://arxiv.org/abs/1401.7320v1
ER -