Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k
-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k
-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k
-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k
-means on the entire data set—appears to be challenging.
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