Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network
Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tol...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019-10-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.9.041023 |
| _version_ | 1818727553699938304 |
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| author | Quntao Zhuang Zheshen Zhang |
| author_facet | Quntao Zhuang Zheshen Zhang |
| author_sort | Quntao Zhuang |
| collection | DOAJ |
| description | Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. However, there also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled enhancement. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford. |
| first_indexed | 2024-12-17T22:15:56Z |
| format | Article |
| id | doaj.art-7ad5e7fbb5a64bc4bf8a793c2971d130 |
| institution | Directory Open Access Journal |
| issn | 2160-3308 |
| language | English |
| last_indexed | 2024-12-17T22:15:56Z |
| publishDate | 2019-10-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj.art-7ad5e7fbb5a64bc4bf8a793c2971d1302022-12-21T21:30:36ZengAmerican Physical SocietyPhysical Review X2160-33082019-10-019404102310.1103/PhysRevX.9.041023Physical-Layer Supervised Learning Assisted by an Entangled Sensor NetworkQuntao ZhuangZheshen ZhangMany existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. However, there also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled enhancement. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.http://doi.org/10.1103/PhysRevX.9.041023 |
| spellingShingle | Quntao Zhuang Zheshen Zhang Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network Physical Review X |
| title | Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network |
| title_full | Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network |
| title_fullStr | Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network |
| title_full_unstemmed | Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network |
| title_short | Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network |
| title_sort | physical layer supervised learning assisted by an entangled sensor network |
| url | http://doi.org/10.1103/PhysRevX.9.041023 |
| work_keys_str_mv | AT quntaozhuang physicallayersupervisedlearningassistedbyanentangledsensornetwork AT zheshenzhang physicallayersupervisedlearningassistedbyanentangledsensornetwork |