Pointwise and dynamic programming control synthesis for finite-level open quantum memory systems

TL;DR

This paper develops control synthesis methods for finite-level quantum memory systems affected by noise, using pointwise and dynamic programming approaches to optimize memory retention.

Contribution

It introduces a novel control design framework based on pointwise and dynamic programming for quantum memory systems with algebraic structure.

Findings

Control signal minimizes mean-square deviation of quantum variables.

Derived a Hamilton-Jacobi-Bellman equation for finite-horizon control.

Outlined a recursive asymptotic expansion solution for the HJB equation.

Abstract

This paper is concerned with finite-level quantum memory systems for retaining initial dynamic variables in the presence of external quantum noise. The system variables have an algebraic structure, similar to that of the Pauli matrices, and their Heisenberg picture evolution is governed by a quasilinear quantum stochastic differential equation. The latter involves a Hamiltonian whose parameters depend affinely on a classical control signal in the form of a deterministic function of time. The memory performance is quantified by a mean-square deviation of quantum system variables of interest from their initial conditions. We relate this functional to a matrix-valued state of an auxiliary classical control-affine dynamical system. This leads to a pointwise control design where the control signal minimises the time-derivative of the mean-square deviation with an additional quadratic penalty on the control. In an alternative finite-horizon setting with a terminal-integral cost functional, we apply dynamic programming and obtain a quadratically nonlinear Hamilton-Jacobi-Bellman equation, for which a solution is outlined in the form of a recursively computed asymptotic expansion.