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We consider the evolution of a quantum simple harmonic oscillator in a general Gaussian state under simultaneous time-continuous weak position and momentum measurements. We deduce the stochastic evolution equations for position and momentum…

Quantum Physics · Physics 2022-03-16 Tathagata Karmakar , Philippe Lewalle , Andrew N. Jordan

We study a continuous time stochastic optimal control problem under partial observations that are available only at discrete time instants. This hybrid setting, with continuous dynamics and intermittent noisy measurements, arises in…

Optimization and Control · Mathematics 2026-01-01 Christian Bayer , Saifeddine Ben naamia , Erik von Schwerin , Raul Tempone

A stochastic procedure is developed which allows one to express Pontryagin's maximum principle for dissipative quantum system solely in terms of stochastic wave functions. Time-optimal controls can be efficiently computed without computing…

Quantum Physics · Physics 2020-11-09 Chungwei Lin , Dries Sels , Yanting Ma , Yebin Wang

Quantum optimal control is a set of methods for designing time-varying electromagnetic fields to perform operations in quantum technologies. This tutorial paper introduces the basic elements of this theory based on the Pontryagin maximum…

Quantum Physics · Physics 2024-06-17 Q. Ansel , E. Dionis , F. Arrouas , B. Peaudecerf , S. Guérin , D. Guéry-Odelin , D. Sugny

Optimal Control Theory is a powerful mathematical tool, which has known a rapid development since the 1950s, mainly for engineering applications. More recently, it has become a widely used method to improve process performance in quantum…

Quantum Physics · Physics 2021-09-16 U. Boscain , M. Sigalotti , D. Sugny

In this paper, the Pontryagin-type maximum principle for optimal control of quantum stochastic systems in fermion fields is obtained. These systems have gained significant prominence in numerous quantum applications ranging from physical…

Optimization and Control · Mathematics 2024-06-13 Penghui Wang , Shan Wang

We apply the theory of optimal control to the dynamics of two "gmon" qubits, with the goal of preparing a desired entangled ground state from an initial unentangled one. Given an initial state, a target state, and a Hamiltonian with a set…

Quantum Physics · Physics 2018-07-02 Seraph Bao , Silken Kleer , Ruoyu Wang , Armin Rahmani

For a class of stochastic delay evolution equations driven by cylindrical $Q$-Wiener process, we study the Pontryagin's maximum principle for the stochastic recursive optimal control problem. The delays are given as moving averages with…

Optimization and Control · Mathematics 2024-01-09 Guomin Liu , Jian Song , Meng Wang

For a class of path-dependent stochastic evolution equations driven by cylindrical $Q$-Wiener process, we study the Pontryagin's maximum principle for the stochastic recursive optimal control problem. In this infinite-dimensional control…

Optimization and Control · Mathematics 2025-11-07 Guomin Liu , Jian Song , Meng Wang

This paper is concerned with the partial information optimal control problem of wa controlled forward-backward stochastic differential equation of jump diffusion with correlated noises between the system and the observation. For this type…

Probability · Mathematics 2017-08-28 Qingxin Meng

Reliable high-fidelity quantum state transformation has always been considered as an inseparable part of quantum information processing. In this regard, Pontryagin maximum principle has proved to play an important role to achieve the…

Quantum Physics · Physics 2023-02-21 Nahid Binandeh Dehaghani , A. Pedro Aguiar

We generalize and extend the stochastic path integral formalism and action principle for continuous quantum measurement introduced in [A. Chantasri, J. Dressel and A. N. Jordan, Phys. Rev. A {\bf 88}, 042110 (2013)], where the optimal…

Quantum Physics · Physics 2015-09-23 Areeya Chantasri , Andrew N. Jordan

This paper is concerned with the partial information optimal control problem of mean-field type under partial observation, where the system is given by a controlled mean-field forward-backward stochastic differential equation with…

Optimization and Control · Mathematics 2017-08-21 Qingxin Meng , Qiuhong Shi , Maoning Tang

Despite significant progress in theoretical and laboratory quantum control, engineering quantum systems remains principally challenging due to manifestation of noise and uncertainties associated with the field and Hamiltonian parameters. In…

Quantum Physics · Physics 2021-12-15 Andrew Koswara , Vaibhav Bhutoria , Raj Chakrabarti

IIn this paper, we study a partially observed progressive optimal control problem of forward-backward stochastic differential equations with random jumps, where the control domain is not necessarily convex, and the control variable enter…

Optimization and Control · Mathematics 2022-06-27 Yueyang Zheng , Jingtao Shi

Optimal control theory, also known as Pontryagin's Maximum Principle, is applied to the quantum parameter estimation in the presence of decoherence. An efficient procedure is devised to compute the gradient of quantum Fisher information…

Quantum Physics · Physics 2022-05-03 Chungwei Lin , Yanting Ma , Dries Sels

We apply the methodology of optimal control theory to the problem of implementing quantum gates in continuous variable systems with quadratic Hamiltonians. We demonstrate that it is possible to define a fidelity measure for continuous…

Quantum Physics · Physics 2009-11-13 Rebing Wu , Raj Chakrabarti , Herschel Rabitz

Trajectory optimization is a fundamental stochastic optimal control problem. This paper deals with a trajectory optimization approach for dynamical systems subject to measurement noise that can be fitted into linear time-varying stochastic…

Systems and Control · Electrical Eng. & Systems 2021-08-24 Prakash Mallick , Zhiyong Chen

This work establishes two versions of the Pontryagin-type maximum principles for partially observed optimal control of coupled forward stochastic partial differential equations (FSPDEs) and backward stochastic differential equations (BSDEs)…

Optimization and Control · Mathematics 2026-03-03 Hongjiang Qian , George Yin , Yanzhao Cao , Guannan Zhang

We present a neural network approach for approximating the value function of high-dimensional stochastic control problems. Our training process simultaneously updates our value function estimate and identifies the part of the state space…

Optimization and Control · Mathematics 2024-05-08 Xingjian Li , Deepanshu Verma , Lars Ruthotto
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