Papers Published
- Johri, S; Debnath, S; Mocherla, A; Singk, A; Prakash, A; Kim, J; Kerenidis, I, Nearest centroid classification on a trapped ion quantum computer,
npj Quantum Information, vol. 7 no. 1
(December, 2021) [doi] .
(last updated on 2025/04/18)Abstract:
Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.