Quantum Optimization Heuristics with an Application to Knapsack Problems

This paper introduces two techniques that make the standard Quantum Approximate Optimization Algorithm (QAOA) more suitable for constrained optimization problems. The first technique describes how to use the outcome of a prior greedy classical algorithm to define an initial quantum state and mixing...

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Bibliographic Details
Published in2021 IEEE International Conference on Quantum Computing and Engineering (QCE) pp. 160 - 170
Main Authors Dam, Wim van, Eldefrawy, Karim, Genise, Nicholas, Parham, Natalie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2021
Subjects
Approximation algorithms
Conferences
Optimization
Quantum algorithm
Quantum circuit
Quantum state
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DOI10.1109/QCE52317.2021.00033

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Summary:This paper introduces two techniques that make the standard Quantum Approximate Optimization Algorithm (QAOA) more suitable for constrained optimization problems. The first technique describes how to use the outcome of a prior greedy classical algorithm to define an initial quantum state and mixing operation to adjust the quantum optimization algorithm to explore the possible answers around this initial greedy solution. The second technique is used to nudge the quantum exploration to avoid the local minima around the greedy solutions. To analyze the benefits of these two techniques we run the quantum algorithm on known hard instances of the Knapsack Problem using unit depth quantum circuits. The results show that the adjusted quantum optimization heuristics typically perform better than various classical heuristics.
DOI:10.1109/QCE52317.2021.00033