107,183 research outputs found
The pointer basis and the feedback stabilization of quantum systems
The dynamics for an open quantum system can be `unravelled' in infinitely
many ways, depending on how the environment is monitored, yielding different
sorts of conditioned states, evolving stochastically. In the case of ideal
monitoring these states are pure, and the set of states for a given monitoring
forms a basis (which is overcomplete in general) for the system. It has been
argued elsewhere [D. Atkins et al., Europhys. Lett. 69, 163 (2005)] that the
`pointer basis' as introduced by Zurek and Paz [Phys. Rev. Lett 70,
1187(1993)], should be identified with the unravelling-induced basis which
decoheres most slowly. Here we show the applicability of this concept of
pointer basis to the problem of state stabilization for quantum systems. In
particular we prove that for linear Gaussian quantum systems, if the feedback
control is assumed to be strong compared to the decoherence of the pointer
basis, then the system can be stabilized in one of the pointer basis states
with a fidelity close to one (the infidelity varies inversely with the control
strength). Moreover, if the aim of the feedback is to maximize the fidelity of
the unconditioned system state with a pure state that is one of its conditioned
states, then the optimal unravelling for stabilizing the system in this way is
that which induces the pointer basis for the conditioned states. We illustrate
these results with a model system: quantum Brownian motion. We show that even
if the feedback control strength is comparable to the decoherence, the optimal
unravelling still induces a basis very close to the pointer basis. However if
the feedback control is weak compared to the decoherence, this is not the case
Linear feedback stabilization of a dispersively monitored qubit
The state of a continuously monitored qubit evolves stochastically,
exhibiting competition between coherent Hamiltonian dynamics and diffusive
partial collapse dynamics that follow the measurement record. We couple these
distinct types of dynamics together by linearly feeding the collected record
for dispersive energy measurements directly back into a coherent Rabi drive
amplitude. Such feedback turns the competition cooperative, and effectively
stabilizes the qubit state near a target state. We derive the conditions for
obtaining such dispersive state stabilization and verify the stabilization
conditions numerically. We include common experimental nonidealities, such as
energy decay, environmental dephasing, detector efficiency, and feedback delay,
and show that the feedback delay has the most significant negative effect on
the feedback protocol. Setting the measurement collapse timescale to be long
compared to the feedback delay yields the best stabilization.Comment: 16 pages, 7 figure
Sparse Stabilization and Control of Alignment Models
From a mathematical point of view self-organization can be described as
patterns to which certain dynamical systems modeling social dynamics tend
spontaneously to be attracted. In this paper we explore situations beyond
self-organization, in particular how to externally control such dynamical
systems in order to eventually enforce pattern formation also in those
situations where this wished phenomenon does not result from spontaneous
convergence. Our focus is on dynamical systems of Cucker-Smale type, modeling
consensus emergence, and we question the existence of stabilization and optimal
control strategies which require the minimal amount of external intervention
for nevertheless inducing consensus in a group of interacting agents. We
provide a variational criterion to explicitly design feedback controls that are
componentwise sparse, i.e. with at most one nonzero component at every instant
of time. Controls sharing this sparsity feature are very realistic and
convenient for practical issues. Moreover, the maximally sparse ones are
instantaneously optimal in terms of the decay rate of a suitably designed
Lyapunov functional, measuring the distance from consensus. As a consequence we
provide a mathematical justification to the general principle according to
which "sparse is better" in the sense that a policy maker, who is not allowed
to predict future developments, should always consider more favorable to
intervene with stronger action on the fewest possible instantaneous optimal
leaders rather than trying to control more agents with minor strength in order
to achieve group consensus. We then establish local and global sparse
controllability properties to consensus and, finally, we analyze the sparsity
of solutions of the finite time optimal control problem where the minimization
criterion is a combination of the distance from consensus and of the l1-norm of
the control.Comment: 33 pages, 5 figure
On Reduced Input-Output Dynamic Mode Decomposition
The identification of reduced-order models from high-dimensional data is a
challenging task, and even more so if the identified system should not only be
suitable for a certain data set, but generally approximate the input-output
behavior of the data source. In this work, we consider the input-output dynamic
mode decomposition method for system identification. We compare excitation
approaches for the data-driven identification process and describe an
optimization-based stabilization strategy for the identified systems
Pole Assignment With Improved Control Performance by Means of Periodic Feedback
This technical note is concerned with the pole placement of continuous-time linear time-invariant (LTI) systems by means of LQ suboptimal periodic feedback. It is well-known that there exist infinitely many generalized sampled-data hold functions (GSHF) for any controllable LTI system to place the modes of its discrete-time equivalent model at prescribed locations. Among all such GSHFs, this technical note aims to find the one which also minimizes a given LQ performance index. To this end, the GSHF being sought is written as the sum of a particular GSHF and a homogeneous one. The particular GSHF can be readily obtained using the conventional pole-placement techniques. The homogeneous GSHF, on the other hand, is expressed as a linear combination of a finite number of functions such as polynomials, sinusoidals, etc. The problem of finding the optimal coefficients of this linear combination is then formulated as a linear matrix inequality (LMI) optimization. The procedure is illustrated by a numerical example
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