First quantum machine learning applications on an on-site room-temperature quantum computer (2312.11673v1)
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.
- Data re-uploading for a universal quantum classifier. Quantum, 4:226, 2020.
- Adam: A method for stochastic optimization. arXiv:1412.6980, 2017.
- Machine Learning with Quantum Computers. Quantum Science and Technology. Springer Cham, 2nd edition, October 2021.
- James L. Park. The concept of transition in quantum mechanics. Found. Phys., 1:23, 1970.
- A single quantum cannot be cloned. Nature, 299:802, 1982.
- D. Dieks. Communication by epr devices. Phys. Lett. A, 92:271, 1982.
- Machine learning and deep learning. Electron. Mark., 31:685, 2021.
- Foundations of Machine Learning. The MIT Press, Cambridge, Massachusetts, 2nd edition, 2018.
- OpenAI. Gpt-4 technical report. arXiv:2303.08774, 2023.
- Carbon emissions and large neural network training. arXiv:2104.10350, 2021.
- Alex de Vries. The growing energy footprint of artificial intelligence. Joule, 7:2191, 2023.
- Generalization in quantum machine learning from few training data. Nat. Commun., 13:4919, 2022.
- Quantum machine learning: A review and current status. In Data Management, Analytics and Innovation, page 101, Singapore, 2021. Springer Singapore.
- Quantum advantage in learning from experiments. Science, 376:1182, 2022.
- Quantum utility – definition and assessment of a practical quantum advantage. In 2023 IEEE International Conference on Quantum Software (QSW), page 162, 2023.
- Quantum-inspired machine learning on high-energy physics data. npj Quantum Inf, 7:111, 2021.
- Scott Aaronson. The learnability of quantum states. Proc. R. Soc. Lond. A, 463:3089, 2007.
- Quantum learning and universal quantum matching machine. Phys. Rev. A, 66:022303, Aug 2002.
- Optimal quantum learning of a unitary transformation. Phys. Rev. A, 81:032324, Mar 2010.
- An introduction to quantum machine learning. Contemporary Physics, 56(2):172–185, 2015.
- Quantum machine learning. Nature, 549:195, 2017.
- Quantum machine learning — an overview. Electronics, 12(11), 2023.
- Quantum machine learning: A review and case studies. Entropy, 25(2), 2023.
- Alexander Alodjants Alexey Melnikov, Mohammad Kordzanganeh and Ray-Kuang Lee. Quantum machine learning: from physics to software engineering. Adv. Phys. X, 8(1):2165452, 2023.
- Single-qubit universal classifier implemented on an ion-trap quantum device. Phys. Rev. A, 106:012411, Jul 2022.
- Demonstration of a bosonic quantum classifier with data reuploading. Phys. Rev. Lett., 131, 2023.
- Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator, arxiv:2305.02956, 2023.
- A didactic approach to quantum machine learning with a single qubit. Phys. Scr., 98, 2022.
- Software for massively parallel quantum computing. arXiv:2211.13355, 2022.
- On depth, robustness and performance using the data re-uploading single-qubit classifier. IEEE Access, 9:65127–65139, 2021.
- Mariam Akhtar et. al. in preparation, 2024.
- The nitrogen-vacancy colour centre in diamond. Phys. Rep., 528:1, 2013.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
