First quantum machine learning applications on an on-site room-temperature quantum computer

• URL: http://arxiv.org/abs/2312.11673v1
• Date: Mon, 18 Dec 2023 19:30:26 GMT
• Title: First quantum machine learning applications on an on-site room-temperature quantum computer
• Authors: Nils Herrmann, Mariam Akhtar, Daanish Arya, Marcus W. Doherty, Pascal Macha, Florian Preis, Stefan Prestel, Michael L. Walker
• Abstract summary: We demonstrate the application of a quantum machine learning (QML) algorithm on an on-site room-temperature quantum computer. A two-qubit quantum computer installed at the Pawsey Supercomputing Centre in Perth, Australia, is used to solve multi-class classification problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate - for the first time - the application of a quantum machine learning (QML) algorithm on an on-site room-temperature quantum computer. A two-qubit quantum computer installed at the Pawsey Supercomputing Centre in Perth, Australia, is used to solve multi-class classification problems on unseen, i.e. untrained, 2D data points. The underlying 1-qubit model is based on the data re-uploading framework of the universal quantum classifier and was trained on an ideal quantum simulator using the Adam optimiser. No noise models or device-specific insights were used in the training process. The optimised model was deployed to the quantum device by means of a single XYX decomposition leading to three parameterised single qubit rotations. The results for different classification problems are compared to the optimal results of an ideal simulator. The room-temperature quantum computer achieves very high classification accuracies, on par with ideal state vector simulations.

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