{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["006:55"], "contactPoint": {"fn": "Scott Glancy", "hasEmail": "mailto:scott.glancy@nist.gov"}, "description": "This Python software package provides functionality for inference of the initial state of a Hidden Markov Model (HMM), when we have access to permutations of the underlying states. We provide both analytical calculations to compute the probability of correct inference, and functionality for Monte Carlo computations. Further details are provided in arxiv:xxxx.xxxxxx.", "distribution": [{"accessURL": "https://github.com/usnistgov/perm_hmm", "description": "Github repository of Python code for quantum state inference via permutations in Hidden Markov Models", "format": "Python code", "title": "Github Repository"}], "identifier": "ark:/88434/mds2-2573", "issued": "2022-03-21", "keyword": ["quantum information theory", "hidden markov model", "quantum measurement", "trapped ion"], "landingPage": "https://github.com/usnistgov/perm_hmm", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2022-03-08 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "theme": ["Physics:Quantum information science", "Physics:Atomic, molecular, and quantum", "Mathematics and Statistics:Statistical analysis", "Mathematics and Statistics:Numerical methods and software"], "title": "Quantum State Inference Via Permutations In Hidden Markov Models"}