scholarly journals Closed-Loop and Robust Control of Quantum Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chunlin Chen ◽  
Lin-Cheng Wang ◽  
Yuanlong Wang
Keyword(s):  
Robust Control ◽  
Feedback Control ◽  
Quantum Control ◽  
Closed Loop ◽  
Learning Control ◽  
Quantum Systems ◽  
General Idea ◽  
Control Measurement ◽  
Control Of Quantum Systems ◽  
Control Feedback

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control asH∞control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

PROBABILITY DENSITY FUNCTION CONTROL OF QUANTUM SYSTEMS

2011 ◽  
Vol 25 (17) ◽  
pp. 2289-2297 ◽  
Author(s):  
YI-FAN XING ◽  
JUN WU
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Quantum Control ◽  
Quantum Systems ◽  
Quantum Model ◽  
Control Engineering ◽  
Control Of Quantum Systems ◽  
Probability Density Function Pdf ◽  
Specific Control

This paper proposes a new method of controlling quantum systems via probability density function (PDF) control. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed for the general case and limited input energy, respectively. Unlike traditional quantum control methods, this method directly controls the probability distribution of the quantum state. It provides an alternative method for quantum control engineering.

Feedback Control of Linear Distributed Systems

1967 ◽  
Vol 89 (2) ◽  
pp. 379-383 ◽  
Author(s):  
Donald M. Wiberg
Keyword(s):  
Boundary Condition ◽  
Distributed Systems ◽  
Feedback Control ◽  
Closed Loop ◽  
The State ◽  
Loop System ◽  
Closed Loop System ◽  
Loop Equation ◽  
Control Feedback ◽  
And Control

The optimum feedback control of controllable linear distributed stationary systems is discussed. A linear closed-loop system is assured by restricting the criterion to be the integral of quadratics in the state and control. Feedback is obtained by expansion of the linear closed-loop equation in terms of uncoupled modes. By incorporating symbolic functions into the formulation, one can treat boundary condition control and point observable systems that are null-delta controllable.

scholarly journals Closed-loop separation control using machine learning

2015 ◽  
Vol 770 ◽  
pp. 442-457 ◽  
Author(s):  
N. Gautier ◽  
J.-L. Aider ◽  
T. Duriez ◽  
B. R. Noack ◽  
M. Segond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feedback Control ◽  
Genetic Programming ◽  
Closed Loop ◽  
Recirculation Zone ◽  
Low Frequency ◽  
Learning Control ◽  
Separation Control ◽  
Periodic Forcing ◽  
Model Free

We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call ‘machine learning control’. The goal is to reduce the recirculation zone of backward-facing step flow at $\mathit{Re}_{h}=1350$ manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin–Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

scholarly journals Robust control of quantum systems by quantum systems

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Thomas Konrad ◽  
Amy Rouillard ◽  
Michael Kastner ◽  
Hermann Uys
Keyword(s):  
Robust Control ◽  
Quantum Systems ◽  
Control Of Quantum Systems

scholarly journals Principles and applications of quantum control engineering

2012 ◽  
Vol 370 (1979) ◽  
pp. 5241-5258 ◽  
Author(s):  
John E. Gough
Keyword(s):  
Feedback Control ◽  
Royal Society ◽  
Quantum Control ◽  
Quantum Systems ◽  
Control Engineering ◽  
Quantum Feedback

This is a brief survey of quantum feedback control and specifically follows on from the two-day conference Principles and applications of quantum control engineering, which took place in the Kavli Royal Society International Centre at Chicheley Hall, on 12–13 December 2011. This was the eighth in a series of principles and applications of control to quantum systems workshops.

The regime of good control

2009 ◽  
Vol 9 (5&6) ◽  
pp. 395-405
Author(s):  
J. Li ◽  
K. Jacobs
Keyword(s):  
Steady State ◽  
Feedback Control ◽  
Nonlinear Systems ◽  
Equations Of Motion ◽  
Good Control ◽  
Classical Case ◽  
Quantum Systems ◽  
Single Qubit ◽  
Control Of Quantum Systems ◽  
Steady State Performance

We derive the equations of motion describing the feedback control of quantum systems in the regime of ``good control", in which the control is sufficient to keep the system close to the desired state. One can view this regime as the quantum equivalent of the ``linearized" regime for feedback control of classical nonlinear systems. Strikingly, while the dynamics of a single qubit in this regime is indeed linear, that of all larger systems remains nonlinear, in contrast to the classical case. As a first application of these equations, we determine the steady-state performance of feedback protocols for a single qubit that use unbiased measurements.

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