Parametric randomization, complex symplectic factorizations, and quadratic-exponential functionals for Gaussian quantum states

TL;DR

This paper introduces a novel method combining probabilistic and algebraic techniques to efficiently compute quadratic-exponential functionals of Gaussian quantum states, relevant for quantum control and risk assessment.

Contribution

It develops a Lie-algebraic framework and parametric randomization approach for simplifying the calculation of quantum expectations of operator exponentials in Gaussian states.

Findings

Provides a recursive method for quadratic-exponential functional computation.

Reduces quantum expectation calculations to classical Gaussian moments.

Enhances tools for quantum risk-sensitive filtering and control.

Abstract

This paper combines probabilistic and algebraic techniques for computing quantum expectations of operator exponentials (and their products) of quadratic forms of quantum variables in Gaussian states. Such quadratic-exponential functionals (QEFs) resemble quantum statistical mechanical partition functions with quadratic Hamiltonians and are also used as performance criteria in quantum risk-sensitive filtering and control problems for linear quantum stochastic systems. We employ a Lie-algebraic correspondence between complex symplectic matrices and quadratic-exponential functions of system variables of a quantum harmonic oscillator. The complex symplectic factorizations are used together with a parametric randomization of the quasi-characteristic or moment-generating functions according to an auxiliary classical Gaussian distribution. This reduces the QEF to an exponential moment of a quadratic form of classical Gaussian random variables with a complex symmetric matrix and is applicable to recursive computation of such moments.