Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits
Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum devices, parametrized quantum circuits have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally c...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Physical Society
2020
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| _version_ | 1797065136177938432 |
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| author | Cao, S Wossnig, L Vlastakis, B Leek, P Grant, E |
| author_facet | Cao, S Wossnig, L Vlastakis, B Leek, P Grant, E |
| author_sort | Cao, S |
| collection | OXFORD |
| description | Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum devices, parametrized quantum circuits have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state. |
| first_indexed | 2024-03-06T21:24:20Z |
| format | Journal article |
| id | oxford-uuid:428ecc3b-8ca0-4c04-a5d6-ec42d5c06888 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:24:20Z |
| publishDate | 2020 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | oxford-uuid:428ecc3b-8ca0-4c04-a5d6-ec42d5c068882022-03-26T14:50:14ZCost-function embedding and dataset encoding for machine learning with parametrized quantum circuitsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:428ecc3b-8ca0-4c04-a5d6-ec42d5c06888EnglishSymplectic ElementsAmerican Physical Society2020Cao, SWossnig, LVlastakis, BLeek, PGrant, EMachine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum devices, parametrized quantum circuits have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state. |
| spellingShingle | Cao, S Wossnig, L Vlastakis, B Leek, P Grant, E Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title | Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title_full | Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title_fullStr | Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title_full_unstemmed | Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title_short | Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| title_sort | cost function embedding and dataset encoding for machine learning with parametrized quantum circuits |
| work_keys_str_mv | AT caos costfunctionembeddinganddatasetencodingformachinelearningwithparametrizedquantumcircuits AT wossnigl costfunctionembeddinganddatasetencodingformachinelearningwithparametrizedquantumcircuits AT vlastakisb costfunctionembeddinganddatasetencodingformachinelearningwithparametrizedquantumcircuits AT leekp costfunctionembeddinganddatasetencodingformachinelearningwithparametrizedquantumcircuits AT grante costfunctionembeddinganddatasetencodingformachinelearningwithparametrizedquantumcircuits |