169,003 research outputs found
Tensor Network alternating linear scheme for MIMO Volterra system identification
This article introduces two Tensor Network-based iterative algorithms for the
identification of high-order discrete-time nonlinear multiple-input
multiple-output (MIMO) Volterra systems. The system identification problem is
rewritten in terms of a Volterra tensor, which is never explicitly constructed,
thus avoiding the curse of dimensionality. It is shown how each iteration of
the two identification algorithms involves solving a linear system of low
computational complexity. The proposed algorithms are guaranteed to
monotonically converge and numerical stability is ensured through the use of
orthogonal matrix factorizations. The performance and accuracy of the two
identification algorithms are illustrated by numerical experiments, where
accurate degree-10 MIMO Volterra models are identified in about 1 second in
Matlab on a standard desktop pc
Control through operators for quantum chemistry
We consider the problem of operator identification in quantum control. The
free Hamiltonian and the dipole moment are searched such that a given target
state is reached at a given time. A local existence result is obtained. As a
by-product, our works reveals necessary conditions on the laser field to make
the identification feasible. In the last part of this work, some algorithms are
proposed to compute effectively these operators
Exact dimension estimation of interacting qubit systems assisted by a single quantum probe
Estimating the dimension of an Hilbert space is an important component of
quantum system identification. In quantum technologies, the dimension of a
quantum system (or its corresponding accessible Hilbert space) is an important
resource, as larger dimensions determine e.g. the performance of quantum
computation protocols or the sensitivity of quantum sensors. Despite being a
critical task in quantum system identification, estimating the Hilbert space
dimension is experimentally challenging. While there have been proposals for
various dimension witnesses capable of putting a lower bound on the dimension
from measuring collective observables that encode correlations, in many
practical scenarios, especially for multiqubit systems, the experimental
control might not be able to engineer the required initialization, dynamics and
observables.
Here we propose a more practical strategy, that relies not on directly
measuring an unknown multiqubit target system, but on the indirect interaction
with a local quantum probe under the experimenter's control. Assuming only that
the interaction model is given and the evolution correlates all the qubits with
the probe, we combine a graph-theoretical approach and realization theory to
demonstrate that the dimension of the Hilbert space can be exactly estimated
from the model order of the system. We further analyze the robustness in the
presence of background noise of the proposed estimation method based on
realization theory, finding that despite stringent constrains on the allowed
noise level, exact dimension estimation can still be achieved.Comment: v3: accepted version. We would like to offer our gratitudes to the
editors and referees for their helpful and insightful opinions and feedback
Maximum Entropy Vector Kernels for MIMO system identification
Recent contributions have framed linear system identification as a
nonparametric regularized inverse problem. Relying on -type
regularization which accounts for the stability and smoothness of the impulse
response to be estimated, these approaches have been shown to be competitive
w.r.t classical parametric methods. In this paper, adopting Maximum Entropy
arguments, we derive a new penalty deriving from a vector-valued
kernel; to do so we exploit the structure of the Hankel matrix, thus
controlling at the same time complexity, measured by the McMillan degree,
stability and smoothness of the identified models. As a special case we recover
the nuclear norm penalty on the squared block Hankel matrix. In contrast with
previous literature on reweighted nuclear norm penalties, our kernel is
described by a small number of hyper-parameters, which are iteratively updated
through marginal likelihood maximization; constraining the structure of the
kernel acts as a (hyper)regularizer which helps controlling the effective
degrees of freedom of our estimator. To optimize the marginal likelihood we
adapt a Scaled Gradient Projection (SGP) algorithm which is proved to be
significantly computationally cheaper than other first and second order
off-the-shelf optimization methods. The paper also contains an extensive
comparison with many state-of-the-art methods on several Monte-Carlo studies,
which confirms the effectiveness of our procedure
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