Variational quantum unsampling on a quantum photonic processor
© 2020, The Author(s), under exclusive licence to Springer Nature Limited. A promising route towards the demonstration of near-term quantum advantage (or supremacy) over classical systems relies on running tailored quantum algorithms on noisy intermediate-scale quantum machines. These algorithms typ...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/136481 |
| _version_ | 1811078489815646208 |
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| author | Carolan, Jacques Mohseni, Masoud Olson, Jonathan P Prabhu, Mihika Chen, Changchen Bunandar, Darius Niu, Murphy Yuezhen Harris, Nicholas C Wong, Franco NC Hochberg, Michael Lloyd, Seth Englund, Dirk |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Carolan, Jacques Mohseni, Masoud Olson, Jonathan P Prabhu, Mihika Chen, Changchen Bunandar, Darius Niu, Murphy Yuezhen Harris, Nicholas C Wong, Franco NC Hochberg, Michael Lloyd, Seth Englund, Dirk |
| author_sort | Carolan, Jacques |
| collection | MIT |
| description | © 2020, The Author(s), under exclusive licence to Springer Nature Limited. A promising route towards the demonstration of near-term quantum advantage (or supremacy) over classical systems relies on running tailored quantum algorithms on noisy intermediate-scale quantum machines. These algorithms typically involve sampling from probability distributions that—under plausible complexity-theoretic conjectures—cannot be efficiently generated classically. Rather than determining the computational features of output states produced by a given physical system, we investigate what features of the generating system can be efficiently learnt given direct access to an output state. To tackle this question, here we introduce the variational quantum unsampling protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuit outputs. In our approach, one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state, essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. We experimentally demonstrate the variational quantum unsampling protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications, including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation. |
| first_indexed | 2024-09-23T11:00:46Z |
| format | Article |
| id | mit-1721.1/136481 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:00:46Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1364812023-12-22T19:50:35Z Variational quantum unsampling on a quantum photonic processor Carolan, Jacques Mohseni, Masoud Olson, Jonathan P Prabhu, Mihika Chen, Changchen Bunandar, Darius Niu, Murphy Yuezhen Harris, Nicholas C Wong, Franco NC Hochberg, Michael Lloyd, Seth Englund, Dirk Massachusetts Institute of Technology. Research Laboratory of Electronics Massachusetts Institute of Technology. Department of Mechanical Engineering © 2020, The Author(s), under exclusive licence to Springer Nature Limited. A promising route towards the demonstration of near-term quantum advantage (or supremacy) over classical systems relies on running tailored quantum algorithms on noisy intermediate-scale quantum machines. These algorithms typically involve sampling from probability distributions that—under plausible complexity-theoretic conjectures—cannot be efficiently generated classically. Rather than determining the computational features of output states produced by a given physical system, we investigate what features of the generating system can be efficiently learnt given direct access to an output state. To tackle this question, here we introduce the variational quantum unsampling protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuit outputs. In our approach, one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state, essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. We experimentally demonstrate the variational quantum unsampling protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications, including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation. 2021-10-27T20:35:36Z 2021-10-27T20:35:36Z 2020 2020-07-30T16:51:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136481 en 10.1038/S41567-019-0747-6 Nature Physics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Springer Science and Business Media LLC arXiv |
| spellingShingle | Carolan, Jacques Mohseni, Masoud Olson, Jonathan P Prabhu, Mihika Chen, Changchen Bunandar, Darius Niu, Murphy Yuezhen Harris, Nicholas C Wong, Franco NC Hochberg, Michael Lloyd, Seth Englund, Dirk Variational quantum unsampling on a quantum photonic processor |
| title | Variational quantum unsampling on a quantum photonic processor |
| title_full | Variational quantum unsampling on a quantum photonic processor |
| title_fullStr | Variational quantum unsampling on a quantum photonic processor |
| title_full_unstemmed | Variational quantum unsampling on a quantum photonic processor |
| title_short | Variational quantum unsampling on a quantum photonic processor |
| title_sort | variational quantum unsampling on a quantum photonic processor |
| url | https://hdl.handle.net/1721.1/136481 |
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