Controlling nonergodicity in quantum many-body systems by reinforcement learning

• URL: http://arxiv.org/abs/2408.11989v2
• Date: Mon, 9 Sep 2024 21:03:46 GMT
• Title: Controlling nonergodicity in quantum many-body systems by reinforcement learning
• Authors: Li-Li Ye, Ying-Cheng Lai,
• Abstract summary: We develop a model-free and deep-reinforcement learning framework for quantum nonergodicity control. We use the paradigmatic one-dimensional tilted Fermi-Hubbard system to demonstrate that the DRL agent can efficiently learn the quantum many-body system. The continuous control protocols and observations are experimentally feasible.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finding optimal control strategies to suppress quantum thermalization for arbitrarily initial states, the so-called quantum nonergodicity control, is important for quantum information science and technologies. Previous control methods largely relied on theoretical model of the target quantum system, but invertible model approximations and inaccuracies can lead to control failures. We develop a model-free and deep-reinforcement learning (DRL) framework for quantum nonergodicity control. It is a machine-learning method with the unique focus on balancing exploration and exploitation strategies to maximize the cumulative rewards so as to preserve the initial memory in the time-dependent nonergodic metrics over a long stretch of time. We use the paradigmatic one-dimensional tilted Fermi-Hubbard system to demonstrate that the DRL agent can efficiently learn the quantum many-body system solely through the interactions with the environment. The optimal policy obtained by the DRL provides broader control scenarios for managing nonergodicity in the phase diagram as compared to, e.g., the specific protocol for Wannier-Stark localization. The continuous control protocols and observations are experimentally feasible. The model-free nature of DRL and its versatile search space for control functions render promising nonergodicity control in more complex quantum many-body systems.

Related papers

• 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)
• Quantum Gate Generation in Two-Level Open Quantum Systems by Coherent and Incoherent Photons Found with Gradient Search [77.34726150561087]
  We consider an environment formed by incoherent photons as a resource for controlling open quantum systems via an incoherent control. We exploit a coherent control in the Hamiltonian and an incoherent control in the dissipator which induces the time-dependent decoherence rates.
  arXiv  Detail & Related papers  (2023-02-28T07:36:02Z)
• Multi-squeezed state generation and universal bosonic control via a driven quantum Rabi model [68.8204255655161]
  Universal control over a bosonic degree of freedom is key in the quest for quantum-based technologies. Here we consider a single ancillary two-level system, interacting with the bosonic mode of interest via a driven quantum Rabi model. We show that it is sufficient to induce the deterministic realization of a large class of Gaussian and non-Gaussian gates, which in turn provide universal bosonic control.
  arXiv  Detail & Related papers  (2022-09-16T14:18:53Z)
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks [0.0]
  We present a novel framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL) We show that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states, and of adapting control protocols on-thefly when the quantum dynamics is subject to perturbations.
  arXiv  Detail & Related papers  (2022-01-27T20:14:09Z)
• Model predictive control for robust quantum state preparation [4.069849286089743]
  We introduce model predictive control (MPC) for quantum control applications. MPC inherits a natural degree of disturbance rejection by incorporating measurement feedback. We show how MPC can be used to generate practical optimized control sequences.
  arXiv  Detail & Related papers  (2022-01-14T00:55:41Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• Model-Free Quantum Control with Reinforcement Learning [0.0]
  We propose a circuit-based approach for training a reinforcement learning agent on quantum control tasks in a model-free way. We show how to reward the learning agent using measurements of experimentally available observables. This approach significantly outperforms widely used model-free methods in terms of sample efficiency.
  arXiv  Detail & Related papers  (2021-04-29T17:53:26Z)
• Quantum control landscape for ultrafast generation of single-qubit phase shift quantum gates [68.8204255655161]
  We consider the problem of ultrafast controlled generation of single-qubit phase shift quantum gates. Globally optimal control is a control which realizes the gate with maximal possible fidelity. Trap is a control which is optimal only locally but not globally.
  arXiv  Detail & Related papers  (2021-04-26T16:38:43Z)
• Dynamically corrected gates from geometric space curves [55.41644538483948]
  We review a technique for designing control fields that dynamically correct errors while performing operations using a close relationship between quantum evolution and geometric space curves. This approach provides access to the global solution space of control fields that accomplish a given task, facilitating the design of experimentally feasible gate operations for a wide variety of applications.
  arXiv  Detail & Related papers  (2021-03-30T01:12:36Z)
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving [2.5614220901453333]
  CD-QAOA is designed for quantum many-body systems and optimized using a reinforcement learning (RL) approach. We show that using terms occurring in the adiabatic gauge potential as generators of additional control unitaries, it is possible to achieve fast high-fidelity many-body control away from the adiabatic regime. This work paves the way to incorporate recent success from deep learning for the purpose of quantum many-body control.
  arXiv  Detail & Related papers  (2020-10-07T21:13:22Z)
• Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning [0.06372261626436676]
  We propose a multi-level dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies. This framework can be generalized to be applied to other quantum control models.
  arXiv  Detail & Related papers  (2020-07-02T02:11:06Z)

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.