Structured hidden-Markov processes can have exactly flat power spectra, so flat spectra cannot certify randomness; the paper characterizes, constructs, and demonstrates such processes.
Measurement-Induced Randomness and Structure in Controlled Qubit Processes
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abstract
When an experimentalist measures a time series of qubits, the outcomes generate a classical stochastic process. We show that measurement induces high complexity in these processes in two specific senses: they are inherently unpredictable (positive Shannon entropy rate) and they require an infinite number of features for optimal prediction (divergent statistical complexity). We identify nonunifilarity as the mechanism underlying the resulting complexities and examine the influence that measurement choice has on the randomness and structure of measured qubit processes. We introduce new quantitative measures of this complexity and provide efficient algorithms for their estimation.
fields
cond-mat.stat-mech 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Fraudulent White Noise: Flat power spectra belie arbitrarily complex processes
Structured hidden-Markov processes can have exactly flat power spectra, so flat spectra cannot certify randomness; the paper characterizes, constructs, and demonstrates such processes.