dorsal/arxiv
View SchemaBayesian prediction of the Gaussian states from n sample
| Authors | F. Tanaka, F. Komaki |
|---|---|
| Categories | |
| ArXiv ID | quant-ph/0510176 |
| URL | https://arxiv.org/abs/quant-ph/0510176 |
Abstract
Recently quantum prediction problem was proposed in the Bayesian framework. It is shown that Bayesian predictive density operators are the best predictive density operators when we evaluate them by using the average relative entropy based on a prior.As an illustrative example, we treat the Gaussian states family adopting the Gaussian distribution as a prior and give the Bayesian predictive density operator with the heterodyne measurement fixed. We show that it is better than the plug-in predictive density operator based on the maximum likelihood estimate by calculating each average relative entropy.
{
"annotation_id": "963448d2-407d-453a-8ae8-23b20d82c853",
"date_created": "2026-03-02T18:02:20.019000Z",
"date_modified": "2026-03-02T18:02:20.019000Z",
"file_hash": "3a6b63fd9203006f42f12882d412c1ad8f3c92c6dc61b8754124b2730e4b9443",
"private": false,
"record": {
"abstract": "Recently quantum prediction problem was proposed in the Bayesian framework.\nIt is shown that Bayesian predictive density operators are the best predictive\ndensity operators when we evaluate them by using the average relative entropy\nbased on a prior.As an illustrative example, we treat the Gaussian states\nfamily adopting the Gaussian distribution as a prior and give the Bayesian\npredictive density operator with the heterodyne measurement fixed. We show that\nit is better than the plug-in predictive density operator based on the maximum\nlikelihood estimate by calculating each average relative entropy.",
"arxiv_id": "quant-ph/0510176",
"authors": [
"F. Tanaka",
"F. Komaki"
],
"categories": [
"quant-ph"
],
"title": "Bayesian prediction of the Gaussian states from n sample",
"url": "https://arxiv.org/abs/quant-ph/0510176"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "9256e844-ea18-4fc9-b599-2b1c2fe9b7d2",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}