High Dimensional Quantum Machine Learning With Small Quantum Computers
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 wo...
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| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-08-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-08-09-1078/pdf/ |
| _version_ | 1797747411218595840 |
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| author | Simon C. Marshall Casper Gyurik Vedran Dunjko |
| author_facet | Simon C. Marshall Casper Gyurik Vedran Dunjko |
| author_sort | Simon C. Marshall |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T15:50:09Z |
| format | Article |
| id | doaj.art-af19bddf73914158ab655051c34fec90 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-03-12T15:50:09Z |
| publishDate | 2023-08-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-af19bddf73914158ab655051c34fec902023-08-09T15:14:07ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2023-08-017107810.22331/q-2023-08-09-107810.22331/q-2023-08-09-1078High Dimensional Quantum Machine Learning With Small Quantum ComputersSimon C. MarshallCasper GyurikVedran DunjkoQuantum 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.https://quantum-journal.org/papers/q-2023-08-09-1078/pdf/ |
| spellingShingle | Simon C. Marshall Casper Gyurik Vedran Dunjko High Dimensional Quantum Machine Learning With Small Quantum Computers Quantum |
| title | High Dimensional Quantum Machine Learning With Small Quantum Computers |
| title_full | High Dimensional Quantum Machine Learning With Small Quantum Computers |
| title_fullStr | High Dimensional Quantum Machine Learning With Small Quantum Computers |
| title_full_unstemmed | High Dimensional Quantum Machine Learning With Small Quantum Computers |
| title_short | High Dimensional Quantum Machine Learning With Small Quantum Computers |
| title_sort | high dimensional quantum machine learning with small quantum computers |
| url | https://quantum-journal.org/papers/q-2023-08-09-1078/pdf/ |
| work_keys_str_mv | AT simoncmarshall highdimensionalquantummachinelearningwithsmallquantumcomputers AT caspergyurik highdimensionalquantummachinelearningwithsmallquantumcomputers AT vedrandunjko highdimensionalquantummachinelearningwithsmallquantumcomputers |