dorsal/arxiv
View SchemaQuantum Factor Graphs
| Authors | Matthew G. Parker |
|---|---|
| Categories | |
| ArXiv ID | quant-ph/0010043 |
| URL | https://arxiv.org/abs/quant-ph/0010043 |
Abstract
The natural Hilbert Space of quantum particles can implement maximum-likelihood (ML) decoding of classical information. The 'Quantum Product Algorithm' (QPA) is computed on a Factor Graph, where function nodes are unitary matrix operations followed by appropriate quantum measurement. QPA is like the Sum-Product Algorithm (SPA), but without summary, giving optimal decode with exponentially finer detail than achievable using SPA. Graph cycles have no effect on QPA performance. QPA must be repeated a number of times before successful and the ML codeword is obtained only after repeated quantum 'experiments'. ML amplification improves decoding accuracy, and Distributed QPA facilitates successful evolution.
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"date_created": "2026-03-02T18:01:42.193000Z",
"date_modified": "2026-03-02T18:01:42.193000Z",
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"abstract": "The natural Hilbert Space of quantum particles can implement\nmaximum-likelihood (ML) decoding of classical information. The \u0027Quantum Product\nAlgorithm\u0027 (QPA) is computed on a Factor Graph, where function nodes are\nunitary matrix operations followed by appropriate quantum measurement. QPA is\nlike the Sum-Product Algorithm (SPA), but without summary, giving optimal\ndecode with exponentially finer detail than achievable using SPA. Graph cycles\nhave no effect on QPA performance. QPA must be repeated a number of times\nbefore successful and the ML codeword is obtained only after repeated quantum\n\u0027experiments\u0027. ML amplification improves decoding accuracy, and Distributed QPA\nfacilitates successful evolution.",
"arxiv_id": "quant-ph/0010043",
"authors": [
"Matthew G. Parker"
],
"categories": [
"quant-ph"
],
"title": "Quantum Factor Graphs",
"url": "https://arxiv.org/abs/quant-ph/0010043"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "f0578817-ea0d-4ff2-b85f-ff967a1fd263",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
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