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We study an optimal process control problem with multiple assignable causes. The process is initially in-control but is subject to random transition to one of multiple out-of-control states due to assignable causes. The objective is to find…

Optimization and Control · Mathematics 2012-12-12 Jue Wang , Chi-Guhn Lee

This article provides a review of recent developments in the formulation and execution of optimal control strategies for the dynamics of quantum systems. A brief introduction to the concept of optimal control, the dynamics of of open…

Quantum Physics · Physics 2009-10-06 Robert Roloff , Markus Wenin , Walter Pötz

Returning a system to a desired state under a force field involves a thermodynamic cost, i.e., {\it work}. This cost fluctuates for a small-scale system from one experimental realization to another. We introduce a general framework to…

Statistical Mechanics · Physics 2022-12-07 Deepak Gupta , Carlos A. Plata

The optimal control of passive systems in equilibrium typically favours quasistatic (infinite-time) protocols. We show that a breakdown of quasistatic optimality occurs when the controller itself is dissipative. Concretely, we study a…

Statistical Mechanics · Physics 2026-05-08 Luca Cocconi , Henry Alston , Thibault Bertrand

Systems that are driven by a randomly timed, external protocol can seemingly violate the second law of thermodynamics. We show that this thermodynamic paradox is resolved if the outcome of the random time is stored in a memory device.…

Statistical Mechanics · Physics 2025-10-28 Izaak Neri

In this article we analyze the optimal control strategy for rotating a monitored qubit from an initial pure state to an orthogonal state in minimum time. This strategy is described for two different cost functions of interest which do not…

Optimization and Control · Mathematics 2012-11-27 Srinivas Sridharan , Masahiro Yanagisawa , Joshua Combes

A robust-to-dynamics optimization (RDO) problem is an optimization problem specified by two pieces of input: (i) a mathematical program (an objective function $f:\mathbb{R}^n\rightarrow\mathbb{R}$ and a feasible set…

Optimization and Control · Mathematics 2023-11-27 Amir Ali Ahmadi , Oktay Gunluk

Proportional-integral-derivative (PID) control, the most common control strategy in the industry, always suffers from health problems resulting from external disturbances, improper tuning, etc. Therefore, there have been many studies on…

Systems and Control · Electrical Eng. & Systems 2021-08-03 Wei Zhang , He Dong , Yunlang Xu , Xiaoping Li

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variation distance ambiguity on the conditional distribution of the controlled process. We formulate the stochastic…

Optimization and Control · Mathematics 2014-02-06 Ioannis Tzortzis , Charalambos D. Charalambous , Themistoklis Charalambous

This paper considers online optimization for a system that performs a sequence of back-to-back tasks. Each task can be processed in one of multiple processing modes that affect the duration of the task, the reward earned, and an additional…

Optimization and Control · Mathematics 2024-01-17 Michael J. Neely

Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing…

In (fully) dynamic set cover, the goal is to maintain an approximately optimal solution to a dynamically evolving instance of set cover, where in each step either an element is added to or removed from the instance. The two main desiderata…

Data Structures and Algorithms · Computer Science 2025-11-12 Sayan Bhattacharya , Ruoxu Cen , Debmalya Panigrahi

Time-constrained decision processes have been ubiquitous in many fundamental applications in physics, biology and computer science. Recently, restart strategies have gained significant attention for boosting the efficiency of…

Machine Learning · Computer Science 2020-07-02 Semih Cayci , Atilla Eryilmaz , R. Srikant

We determine how to optimally reset a superconducting qubit which interacts with a thermal environment in such a way that the coupling strength is tunable. Describing the system in terms of a time-local master equation with time-dependent…

The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global objective function formed by the addition of partial functions locally known…

Optimization and Control · Mathematics 2022-06-07 Damián Marelli , Yong Xu , Minyue Fu , Zenghong Huang

The task of completing jobs with decaying value arises in a number of application areas including healthcare operations, communications engineering, and perishable inventory control. We consider a system in which a single server completes a…

Optimization and Control · Mathematics 2016-09-20 Neal Master , Nicholas Bambos

The minimum amount of thermodynamic work required in order to implement a quantum computation or a quantum state transformation can be quantified using frameworks based on the resource theory of thermodynamics, deeply rooted in the works of…

Quantum Physics · Physics 2026-01-29 Philippe Faist

Conformational fluctuations are believed to play an important role in the process by which transcription factor proteins locate and bind their target site on the genome of a bacterium. Using a simple model, we show that the binding time can…

Soft Condensed Matter · Physics 2008-07-22 Longhua Hu , Alexander Y. Grosberg , Robijn Bruinsma

Recent understanding of the thermodynamics of small-scale systems have enabled the characterization of the thermodynamic requirements of implementing quantum processes for fixed input states. Here, we extend these results to construct…

Quantum Physics · Physics 2021-07-26 Philippe Faist , Mario Berta , Fernando G. S. L. Brandao

Probabilistic control design is founded on the principle that a rational agent attempts to match modelled with an arbitrary desired closed-loop system trajectory density. The framework was originally proposed as a tractable alternative to…

Machine Learning · Computer Science 2023-11-16 Tom Lefebvre
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