Heterogeneous Quantum Computing for Satellite Constellation Optimization: Solving the Weighted K-Clique Problem

TL;DR

This paper introduces a heterogeneous quantum-classical approach to solve large weighted k-clique problems, demonstrating potential quantum speed-up and solution quality improvements for complex satellite constellation optimization tasks.

Contribution

The paper presents a novel hybrid quantum-classical computing stack that enables solving larger NP-hard problems than current quantum hardware allows.

Findings

Successful application to real-world weighted k-clique problem

Demonstrates quantum speed-up potential

Provides insights into quantum machine learning capabilities

Abstract

NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k-clique problem. Through this experiment, we provide insight into the state of quantum machine learning.