Measuring qubits from finite-state controlled sources generically turns simple quantum processes into classical processes whose optimal predictors need an infinite number of states, a divergence traced to measurement-induced nonunifilarity.
Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Among the predictive hidden Markov models that describe a given stochastic process, the {\epsilon}-machine is strongly minimal in that it minimizes every R\'enyi-based memory measure. Quantum models can be smaller still. In contrast with the {\epsilon}-machine's unique role in the classical setting, however, among the class of processes described by pure-state hidden quantum Markov models, there are those for which there does not exist any strongly minimal model. Quantum memory optimization then depends on which memory measure best matches a given problem circumstance.
fields
quant-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Measurement-Induced Randomness and Structure in Controlled Qubit Processes
Measuring qubits from finite-state controlled sources generically turns simple quantum processes into classical processes whose optimal predictors need an infinite number of states, a divergence traced to measurement-induced nonunifilarity.