1IRIF, CNRS – Université Paris Cité, France
2QC Ware, Palo Alto, USA and Paris, France
3School of Informatics, University of Edinburgh, Scotland, UK
4F. Hoffmann La Roche AG
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Abstract
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work, which uses parametrised quantum circuits for data loading and orthogonal neural layers, we introduce three types of quantum transformers for training and inference, including a quantum transformer based on compound matrices, which guarantees a theoretical advantage of the quantum attention mechanism compared to their classical counterpart both in terms of asymptotic run time and the number of model parameters. These quantum architectures can be built using shallow quantum circuits and produce qualitatively different classification models. The three proposed quantum attention layers vary on the spectrum between closely following the classical transformers and exhibiting more quantum characteristics. As building blocks of the quantum transformer, we propose a novel method for loading a matrix as quantum states as well as two new trainable quantum orthogonal layers adaptable to different levels of connectivity and quality of quantum computers. We performed extensive simulations of the quantum transformers on standard medical image datasets that showed competitively, and at times better performance compared to the classical benchmarks, including the best-in-class classical vision transformers. The quantum transformers we trained on these small-scale datasets require fewer parameters compared to standard classical benchmarks. Finally, we implemented our quantum transformers on superconducting quantum computers and obtained encouraging results for up to six qubit experiments.
Featured image: Quantum circuit to execute one attention layer of the Compound Transformer. A matrix data loader followed by an orthogonal quantum layer.
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Cited by
[1] David Peral García, Juan Cruz-Benito, and Francisco José García-Peñalvo, “Systematic Literature Review: Quantum Machine Learning and its applications”, arXiv:2201.04093, (2022).
[2] El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, and Marco Pistoia, “Quantum Deep Hedging”, Quantum 7, 1191 (2023).
[3] Léo Monbroussou, Jonas Landman, Alex B. Grilo, Romain Kukla, and Elham Kashefi, “Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning”, arXiv:2309.15547, (2023).
[4] Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis, André J. Ferreira-Martins, and Samurai Brito, “Improved Financial Forecasting via Quantum Machine Learning”, arXiv:2306.12965, (2023).
[5] Jason Iaconis and Sonika Johri, “Tensor Network Based Efficient Quantum Data Loading of Images”, arXiv:2310.05897, (2023).
[6] Nishant Jain, Jonas Landman, Natansh Mathur, and Iordanis Kerenidis, “Quantum Fourier Networks for Solving Parametric PDEs”, arXiv:2306.15415, (2023).
[7] Daniel Mastropietro, Georgios Korpas, Vyacheslav Kungurtsev, and Jakub Marecek, “Fleming-Viot helps speed up variational quantum algorithms in the presence of barren plateaus”, arXiv:2311.18090, (2023).
[8] Aliza U. Siddiqui, Kaitlin Gili, and Chris Ballance, “Stressing Out Modern Quantum Hardware: Performance Evaluation and Execution Insights”, arXiv:2401.13793, (2024).
The above citations are from SAO/NASA ADS (last updated successfully 2024-02-22 13:37:43). The list may be incomplete as not all publishers provide suitable and complete citation data.
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This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.
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