Different Strategies for Optimization Using the Quantum Adiabatic Algorithm

TL;DR

This paper investigates various strategies to enhance the success probability of the Quantum Adiabatic Algorithm on complex MAX 2-SAT problems using numerical simulations with 20 qubits.

Contribution

It introduces and evaluates three novel strategies—shortening evolution time, starting in excited states, and adding local Hamiltonians—that improve performance on hard instances.

Findings

Decreasing evolution time increases success probability.

Initializing in excited states yields better results.

Adding local Hamiltonians enhances solution likelihood.

Abstract

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.