Asymptotic and Non-Asymptotic Analysis for Hidden Markovian Process with Quantum Hidden System

Abstract

We focus on a data sequence produced by repetitive quantum measurement on an internal hidden quantum system, and call it a hidden Markovian process. Using a quantum version of the Perron-Frobenius theorem, we derive novel upper and lower bounds for the cumulant generating function of the sample mean of the data. Using these bounds, we derive the central limit theorem and large and moderate deviations for the tail probability. Then, we give the asymptotic variance is given by using the second derivative of the cumulant generating function. We also derive another expression for the asymptotic variance by considering the quantum version of the fundamental matrix. Further, we explain how to extend our results to a general probabilistic system.

21 pages, 2 figures; version 14 July 2018

References in corpus (3)

• A generalized no-broadcasting theorem
• Sanov and Central Limit Theorems for output statistics of quantum Markov chains
• A uniform Berry--Esseen theorem on $M$-estimators for geometrically ergodic Markov chains

Cited by in corpus (3)

• Asymptotic Properties for Markovian Dynamics in Quantum Theory and General Probabilistic Theories
• Quantum theory in finite dimension cannot explain every general process with finite memory
• Local Equivalence Problem in Hidden Markov Model