Robust Quantum Control in Closed and Open Systems: Theory and Practice

• URL: http://arxiv.org/abs/2401.00294v2
• Date: Sat, 27 Jul 2024 17:55:20 GMT
• Title: Robust Quantum Control in Closed and Open Systems: Theory and Practice
• Authors: C. A. Weidner, E. A. Reed, J. Monroe, B. Sheller, S. O'Neil, E. Maas, E. A. Jonckheere, F. C. Langbein, S. G. Schirmer,
• Abstract summary: This survey is written for control theorists to provide a review of the current state of quantum control and outline the challenges faced in trying to apply modern robust control to quantum systems. We present issues that arise when applying classical robust control theory to quantum systems, typical methods used by quantum physicists to explore such systems and their robustness, as well as a discussion of open problems to be addressed in the field.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust control of quantum systems is an increasingly relevant field of study amidst the second quantum revolution, but there remains a gap between taming quantum physics and robust control in its modern analytical form that culminated in fundamental performance bounds. With certain exceptions such as quantum optical systems that can be modeled as linear stochastic differential equations, quantum systems are not amenable to linear, time-invariant, measurement-based robust control techniques, and thus novel gap-bridging techniques must be developed. This survey is written for control theorists to provide a review of the current state of quantum control and outline the challenges faced in trying to apply modern robust control to quantum systems. We present issues that arise when applying classical robust control theory to quantum systems, typical methods used by quantum physicists to explore such systems and their robustness, as well as a discussion of open problems to be addressed in the field. We focus on general, practical applications and recent work to enable control researchers to contribute to advancing this burgeoning field.

Related papers

• Entanglement engineering of optomechanical systems by reinforcement learning [0.201915599621727]
  We develop a model-free deep reinforcement-learning approach to entanglement engineering. We employ quantum optomechanical systems with linear or nonlinear photon-phonon interactions to demonstrate the workings of our protocol.
  arXiv  Detail & Related papers  (2024-06-06T23:25:36Z)
• Quantum control by the environment: Turing uncomputability, Optimization over Stiefel manifolds, Reachable sets, and Incoherent GRAPE [56.47577824219207]
  In many practical situations, the controlled quantum systems are open, interacting with the environment. In this note, we briefly review some results on control of open quantum systems using environment as a resource.
  arXiv  Detail & Related papers  (2024-03-20T10:09:13Z)
• Massive quantum systems as interfaces of quantum mechanics and gravity [0.0]
  The traditional view from particle physics is that quantum gravity effects should only become detectable at extremely high energies and small length scales. In recent decades, the size and mass of quantum systems that can be controlled in the laboratory have reached unprecedented scales. This review focuses on proposals where massive quantum systems act as interfaces between quantum mechanics and gravity.
  arXiv  Detail & Related papers  (2023-11-15T18:58:44Z)
• Entanglement-Assisted Quantum Networks: Mechanics, Enabling Technologies, Challenges, and Research Directions [66.27337498864556]
  This paper presents a comprehensive survey of entanglement-assisted quantum networks. It provides a detailed overview of the network structure, working principles, and development stages. It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
  arXiv  Detail & Related papers  (2023-07-24T02:48:22Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Applying classical control techniques to quantum systems: entanglement versus stability margin and other limitations [0.0]
  Development of robust quantum control has been challenging. There are numerous obstacles to applying classical robust control to quantum system. It remains difficult to extract physical insight when classical robust control tools are applied to these systems.
  arXiv  Detail & Related papers  (2022-07-25T17:52:01Z)
• Experimental graybox quantum system identification and control [2.92406842378658]
  We experimentally demonstrate a "graybox" approach to construct a physical model of a quantum system and use it to design optimal control. Our approach combines physics principles with high-accuracy machine learning and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.
  arXiv  Detail & Related papers  (2022-06-24T10:19:55Z)
• Physics-informed neural networks for quantum control [0.0]
  We introduce a computational method for optimal quantum control problems via physics-informed neural networks (PINNs) We apply our methodology to open quantum systems by efficiently solving the state-to-state transfer problem with high probabilities, short-time evolution, and using low-energy consumption controls.
  arXiv  Detail & Related papers  (2022-06-13T16:17:22Z)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• Quantum Non-equilibrium Many-Body Spin-Photon Systems [91.3755431537592]
  dissertation concerns the quantum dynamics of strongly-correlated quantum systems in out-of-equilibrium states. Our main results can be summarized in three parts: Signature of Critical Dynamics, Driven Dicke Model as a Test-bed of Ultra-Strong Coupling, and Beyond the Kibble-Zurek Mechanism.
  arXiv  Detail & Related papers  (2020-07-23T19:05:56Z)
• Quantum Geometric Machine Learning for Quantum Circuits and Control [78.50747042819503]
  We review and extend the application of deep learning to quantum geometric control problems. We demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems. Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
  arXiv  Detail & Related papers  (2020-06-19T19:12:14Z)

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.