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International Journal of Robust and Nonlinear Control

Finite horizon H∞ control for a class of sampled‐data linear quantum systems

Aline I. Maalouf, Ian R. Petersen

Published Oct 6, 2016• 5 citations
Publisher

Abstract

Summary In this paper, a finite horizon H∞ control problem is solved for a class of linear quantum systems using a dynamic game approach for the case of sampled‐data measurements. The methodology adopted involves an equivalence between the quantum problem and two auxiliary classical problems. Copyright © 2016 John Wiley & Sons, Ltd.

DOI
10.1002/rnc.3683

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In this paper, a finite horizon H∞control problem is solved for a class of linear quantum systems using a dynamic game approach for the case of delayed measurements. The methodology adopted involves an equivalence between the quantum problem and an auxiliary classical stochastic problem. Then, the finite horizon H∞control problem for the class of linear quantum systems under consideration is solved for the case of delayed measurements by solving the finite horizon H∞control problem for an equivalent stochastic H∞control problem using some results from a corresponding deterministic problem following a dynamic game approach.
We develop quantum algorithms for optimal control over a prescribed finite time interval (finite horizon), focusing on continuous-time linear-quadratic regulation (LQR). This foundational control problem often requires solving a nonlinear matrix Riccati equation, which can be costly for large systems and long control durations on conventional classical solvers. We instead work directly with the state-costate optimality equations, reformulating the LQR problem as a multiple-shooting linear system. Importantly, we prove a condition-number bound for the system that is independent of the horizon, under stabilizability and detectability assumptions. By incorporating the initial and final conditions into a single linear system, we can solve the subsequent linear system more efficiently with a quantum linear systems subroutine. For a given initial state, our quantum algorithms return classical estimates of the optimal cost and the optimal control inputs at a selected time, without constructing a Riccati solution operator. Given sparse-matrix block-encodings, an initial-state preparation oracle and a fixed number of control inputs, the query and gate complexities scale polylogarithmically with system size and control duration, provided that sparsity, relevant norms, and stability parameters are bounded. The dependence on the target error is inverse-linear up to logarithmic factors. We establish the required bounds uniformly in system size and duration for a family of damped mass-spring chains with two endpoint controls. Numerical experiments support the analysis.