High Dimensional Quantum Machine Learning With Small Quantum Computers

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High Dimensional Quantum Machine Learning With Small Quantum Computers PlatoBlockchain Data Intelligence. Vertical Search. Ai.

Simon C. Marshall, Casper Gyurik, and Vedran Dunjko

Leiden University, Leiden, The Netherlands

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Abstract

Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. To deal with this limitation the community has produced a set of techniques for evaluating large quantum circuits on smaller quantum devices. These techniques work by evaluating many smaller circuits on the smaller machine, that are then combined in a polynomial to replicate the output of the larger machine. This scheme requires more circuit evaluations than are practical for general circuits. However, we investigate the possibility that for certain applications many of these subcircuits are superfluous, and that a much smaller sum is sufficient to estimate the full circuit. We construct a machine learning model that may be capable of approximating the outputs of the larger circuit with much fewer circuit evaluations. We successfully apply our model to the task of digit recognition, using simulated quantum computers much smaller than the data dimension. The model is also applied to the task of approximating a random 10 qubit PQC with simulated access to a 5 qubit computer, even with only relatively modest number of circuits our model provides an accurate approximation of the 10 qubit PQCs output, superior to a neural network attempt. The developed method might be useful for implementing quantum models on larger data throughout the NISQ era.

► BibTeX data

@article{Marshall2023highdimensional, doi = {10.22331/q-2023-08-09-1078}, url = {https://doi.org/10.22331/q-2023-08-09-1078}, title = {High {D}imensional {Q}uantum {M}achine {L}earning {W}ith {S}mall {Q}uantum {C}omputers}, author = {Marshall, Simon C. and Gyurik, Casper and Dunjko, Vedran}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {7}, pages = {1078}, month = aug, year = {2023}<br /> }

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Cited by

[1] Arsenii Senokosov, Alexander Sedykh, Asel Sagingalieva, and Alexey Melnikov, “Quantum machine learning for image classification”, arXiv:2304.09224, (2023).

[2] Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch, Daniel D. Scherer, Axel Plinge, and Christopher Mutschler, “Cutting multi-control quantum gates with ZX calculus”, arXiv:2302.00387, (2023).

[3] Angus Lowe, Matija Medvidović, Anthony Hayes, Lee J. O’Riordan, Thomas R. Bromley, Juan Miguel Arrazola, and Nathan Killoran, “Fast quantum circuit cutting with randomized measurements”, Quantum 7, 934 (2023).

[4] Lirandë Pira and Chris Ferrie, “An Invitation to Distributed Quantum Neural Networks”, arXiv:2211.07056, (2022).

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[6] Cenk Tüysüz, Giuseppe Clemente, Arianna Crippa, Tobias Hartung, Stefan Kühn, and Karl Jansen, “Classical Splitting of Parametrized Quantum Circuits”, arXiv:2206.09641, (2022).

[7] Patrick Huembeli, Giuseppe Carleo, and Antonio Mezzacapo, “Entanglement Forging with generative neural network models”, arXiv:2205.00933, (2022).

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The above citations are from SAO/NASA ADS (last updated successfully 2023-08-10 03:13:01). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-08-10 03:12:54).

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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Source: https://quantum-journal.org/papers/q-2023-08-09-1078/

Time Stamp: August 9, 2023

Time Stamp: Feb 22, 2024

High Dimensional Quantum Machine Learning With Small Quantum Computers

Republished By Plato

Abstract

► BibTeX data

► References

Cited by

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