Quantum state estimation when qubits are lost: a no-data-left-behind approach
We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean estimate (BME) fo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aa65de |
| _version_ | 1797750822665191424 |
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| author | Brian P Williams Pavel Lougovski |
| author_facet | Brian P Williams Pavel Lougovski |
| author_sort | Brian P Williams |
| collection | DOAJ |
| description | We present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean estimate (BME) for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the BME for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity. |
| first_indexed | 2024-03-12T16:39:25Z |
| format | Article |
| id | doaj.art-56fc4de9498e4637a6d421b39280621a |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:39:25Z |
| publishDate | 2017-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-56fc4de9498e4637a6d421b39280621a2023-08-08T14:36:22ZengIOP PublishingNew Journal of Physics1367-26302017-01-0119404300310.1088/1367-2630/aa65deQuantum state estimation when qubits are lost: a no-data-left-behind approachBrian P Williams0https://orcid.org/0000-0001-7158-8217Pavel Lougovski1Quantum Information Science Group, Oak Ridge National Laboratory , Oak Ridge, TN 37831 United States of AmericaQuantum Information Science Group, Oak Ridge National Laboratory , Oak Ridge, TN 37831 United States of AmericaWe present an approach to Bayesian mean estimation of quantum states using hyperspherical parametrization and an experiment-specific likelihood which allows utilization of all available data, even when qubits are lost. With this method, we report the first closed-form Bayesian mean estimate (BME) for the ideal single qubit. Due to computational constraints, we utilize numerical sampling to determine the BME for a photonic two-qubit experiment in which our novel analysis reduces burdens associated with experimental asymmetries and inefficiencies. This method can be applied to quantum states of any dimension and experimental complexity.https://doi.org/10.1088/1367-2630/aa65dequantum state estimationBayesianqubitinferenceslice samplingMonte Carlo |
| spellingShingle | Brian P Williams Pavel Lougovski Quantum state estimation when qubits are lost: a no-data-left-behind approach New Journal of Physics quantum state estimation Bayesian qubit inference slice sampling Monte Carlo |
| title | Quantum state estimation when qubits are lost: a no-data-left-behind approach |
| title_full | Quantum state estimation when qubits are lost: a no-data-left-behind approach |
| title_fullStr | Quantum state estimation when qubits are lost: a no-data-left-behind approach |
| title_full_unstemmed | Quantum state estimation when qubits are lost: a no-data-left-behind approach |
| title_short | Quantum state estimation when qubits are lost: a no-data-left-behind approach |
| title_sort | quantum state estimation when qubits are lost a no data left behind approach |
| topic | quantum state estimation Bayesian qubit inference slice sampling Monte Carlo |
| url | https://doi.org/10.1088/1367-2630/aa65de |
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