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One of the major benefits of quantum computing is the potential to resolvecomplex computational problems faster than can be done by classical methods.There are many prototype-based clustering methods in use today, and selectionof the starting nodes for the center points is often done randomly. Forprototype-based clustering algorithms, this could lead to much slowerconvergence times. One of the causes of this may be prototype-based clusteringaccepting a local minima as a valid solution when there are possibly bettersolutions. Quantum computing, specifically quantum annealing, offers a solutionto these problems by mapping the initial centroid problem to an IsingHamiltonian where over time the lowest energy in the spectrum correlates to avalid, but better solution. A first approach to this problem utilizing quantumannealing was known as Quantum Optimized Centroid Initialization (QOCI), butthis approach has some limitations both in results and performance. We willpresent a modification of QOCI known as Adaptive Quantum Optimized CentroidInitialization (AQOCI) which addresses many of the limitations in QOCI. Theresults presented are comparable to those obtained using classical techniquesas well as being superior to those results found using QOCI.

Adaptive Quantum Optimized Centroid Initialization