研究目的
Investigating risk-sensitive performance criteria for linear quantum stochastic systems, specifically focusing on quadratic-exponential functionals and their computation, minimization, and application to large deviations estimates and quantum control problems.
研究成果
The paper develops methods for analyzing quadratic-exponential functionals in linear quantum systems, showing their non-reducibility to classical counterparts and providing approximations and bounds for risk-sensitive control applications, with potential extensions to coherent quantum control settings.
研究不足
The study is limited to linear quantum stochastic systems with Gaussian states and specific initial conditions; higher-order approximations involve complex combinatorial structures, and large deviations estimates provide only upper bounds due to the noncommutative nature of quantum variables.
1:Experimental Design and Method Selection:
The study employs theoretical and mathematical analysis of linear quantum stochastic systems using Hudson-Parthasarathy quantum stochastic differential equations (QSDEs), Gaussian quantum states, and integro-differential equations for quadratic-exponential functionals. Methods include Lie-algebraic techniques, combinatorial analysis of permutations, and frequency-domain representations.
2:Sample Selection and Data Sources:
The analysis is based on a two-mode open quantum harmonic oscillator (OQHO) model with specific parameters for energy and coupling matrices, initialized at the invariant Gaussian state driven by vacuum bosonic fields.
3:List of Experimental Equipment and Materials:
No physical equipment is used; the study is purely theoretical, utilizing mathematical models and numerical computations.
4:Experimental Procedures and Operational Workflow:
Steps involve deriving equations for the time evolution of the quadratic-exponential functional, approximating it using quartic and higher-order cumulants, solving algebraic Lyapunov equations, and performing numerical simulations for a specific example.
5:Data Analysis Methods:
Data analysis includes solving algebraic equations, computing asymptotic growth rates, and evaluating large deviations bounds using combinatorial and Fourier transform techniques.
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