Optimal State Preparation for Impulse Estimation in Gaussian Quantum Systems
- URL: http://arxiv.org/abs/2605.12155v1
- Date: Tue, 12 May 2026 14:07:55 GMT
- Title: Optimal State Preparation for Impulse Estimation in Gaussian Quantum Systems
- Abstract summary: We present an optimal control-based strategy to enhance the estimation of impulse-like disturbances in continuously monitored linear classical and quantum systems.<n>Applying to nanomechanical resonators and levitated nanoparticles, optimal parametric driving reduces estimation variance by up to a factor of two relative to steady-state operation.
- Score: 3.6792528307955865
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present an optimal control-based strategy to enhance the estimation of impulse-like disturbances in continuously monitored linear classical and quantum systems by exploiting non-equilibrium states. Using optimal estimation techniques for linear Gaussian systems to collect information from the temporal vicinity of the disturbance, we cast the minimization of disturbance estimation uncertainty as a nonlinear optimal control problem over time-dependent system parameters. The resulting method dynamically shapes the estimation covariances through parametric modulation, maximizing information gain at a known impulse time. This differs fundamentally from conventional squeezing protocols using periodic modulation that effectively degrade inference of impulse-like disturbances. Applied to nanomechanical resonators and levitated nanoparticles, optimal parametric driving reduces estimation variance by up to a factor of two relative to steady-state operation
Related papers
- Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
arXiv Detail & Related papers (2025-03-24T03:19:45Z) - Regime-Aware Time Weighting for Physics-Informed Neural Networks [0.0]
We introduce a novel method to handle the time dimension when PINNs are used to solve time-dependent differential equations.<n>Our approach is grounded in theoretical insights derived from the Lyapunovs, which quantify the sensitivity of solutions to perturbations over time.<n> Numerical experiments on challenging benchmarks, including the chaotic Lorenz system and the Burgers' equation, demonstrate the effectiveness and robustness of the proposed method.
arXiv Detail & Related papers (2024-07-31T14:41:40Z) - Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding [20.43835169613882]
This paper proposes an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear systems.<n>It reveals an infinite-dimensional feature representation induced by the system's nonlinear dynamics, enabling a linear representation of the state-action value function.<n>For practical implementation, this representation is approximated using finite-dimensional truncations.
arXiv Detail & Related papers (2023-04-08T04:23:46Z) - Optimal control for state preparation in two-qubit open quantum systems
driven by coherent and incoherent controls via GRAPE approach [77.34726150561087]
We consider a model of two qubits driven by coherent and incoherent time-dependent controls.
The dynamics of the system is governed by a Gorini-Kossakowski-Sudarshan-Lindblad master equation.
We study evolution of the von Neumann entropy, purity, and one-qubit reduced density matrices under optimized controls.
arXiv Detail & Related papers (2022-11-04T15:20:18Z) - Compensating for non-linear distortions in controlled quantum systems [0.0]
Distortion of the input fields in an experimental platform alters the model accuracy and disturbs the predicted dynamics.
We present an effective method for estimating these distortions which is suitable for non-linear transfer functions of arbitrary lengths and magnitudes.
We have successfully tested our approach for a numerical example of a single Rydberg atom system.
arXiv Detail & Related papers (2022-10-14T14:02:39Z) - A Priori Denoising Strategies for Sparse Identification of Nonlinear
Dynamical Systems: A Comparative Study [68.8204255655161]
We investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements.
We show that, in general, global methods, which use the entire measurement data set, outperform local methods, which employ a neighboring data subset around a local point.
arXiv Detail & Related papers (2022-01-29T23:31:25Z) - Structure-Preserving Learning Using Gaussian Processes and Variational
Integrators [62.31425348954686]
We propose the combination of a variational integrator for the nominal dynamics of a mechanical system and learning residual dynamics with Gaussian process regression.
We extend our approach to systems with known kinematic constraints and provide formal bounds on the prediction uncertainty.
arXiv Detail & Related papers (2021-12-10T11:09:29Z) - Gaussian Process-based Min-norm Stabilizing Controller for
Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
We propose a novel compound kernel that captures the control-affine nature of the problem.
We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
arXiv Detail & Related papers (2020-11-14T01:27:32Z) - Assessment of weak-coupling approximations on a driven two-level system
under dissipation [58.720142291102135]
We study a driven qubit through the numerically exact and non-perturbative method known as the Liouville-von equation with dissipation.
We propose a metric that may be used in experiments to map the regime of validity of the Lindblad equation in predicting the steady state of the driven qubit.
arXiv Detail & Related papers (2020-11-11T22:45:57Z) - Neural Stochastic Contraction Metrics for Learning-based Control and
Estimation [13.751135823626493]
The NSCM framework allows autonomous agents to approximate optimal stable control and estimation policies in real-time.
It outperforms existing nonlinear control and estimation techniques including the state-dependent Riccati equation, iterative LQR, EKF, and the neural contraction.
arXiv Detail & Related papers (2020-11-06T03:04:42Z) - Continuous Lyapunov Controller and Chaotic Non-linear System
Optimization using Deep Machine Learning [0.0]
We present a novel approach for detecting early failure indicators of non-linear highly chaotic system.
The approach proposed continuously monitors the system and controller signals.
The deep neural model predicts the parameter values that would best counteract the expected system in-stability.
arXiv Detail & Related papers (2020-10-28T04:45:12Z) - Neural Contraction Metrics for Robust Estimation and Control: A Convex
Optimization Approach [6.646482960350819]
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM)
The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric.
We demonstrate how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality.
arXiv Detail & Related papers (2020-06-08T05:29:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.