QC Ware and IonQ Experiment Demonstrates Machine Learning Algorithms Can Run on Near-Term Quantum…

--ListenShareWelcome to QC Ware’s Medium channel. We’ll kick things off by sharing with you a major milestone in quantum machine learning that has significant implications for the development of practical and useful applications of QML and for the potential of the next generation of quantum machines to outperform classical computers.Today at Q2B20, Iordanis Kerenidis, head of Quantum Algorithms — International at QC Ware, and Sonika Johri, Senior Quantum Applications Research Scientist at IonQ, will share the technical details of our recent collaborative experiment that demonstrated machine learning on near-term quantum computers can achieve the same or better level of accuracy and can aspire to be faster than on classical computers.Their session, entitled “Noise-Resilient Implementation of a Quantum Nearest Centroid Algorithm,” will be held at 12:20 PM Pacific Time today. A related paper, “Nearest Centroid Classification on a Trapped Ion Quantum Computer,” appeared today as an ArXiv preprint. You can view the paper at: https://arxiv.org/abs/2012.04145.In our experiment, the QC Ware and IonQ teams ran QC Ware’s machine learning algorithm for classification on IonQ’s 11 qubit system. We overcame what was an insurmountable hurdle in QML — loading classical data onto quantum states in a noise-resilient way to allow efficient and robust QML applications. Central to the success of our experiment are QC Ware’s own Forge Data Loader™ technology, which optimally transforms classical data onto quantum states, and the high-quality, fully connected qubits of IonQ’s 11 qubit system.We’ve outlined the details of our experiment below.Experiment DetailsChallengeSolutionResults compared with classical machine learning algorithms running on classical hardwareIndustry impactWe foresee our joint experiment with IonQ to have the following potential impact on the industry: