Preparing Spin Squeezed States via Adaptive Genetic Algorithm

• URL: http://arxiv.org/abs/2410.15375v1
• Date: Sun, 20 Oct 2024 12:15:11 GMT
• Title: Preparing Spin Squeezed States via Adaptive Genetic Algorithm
• Authors: Yiming Zhao, Libo Chen, Yong Wang, Hongyang Ma, Xiaolong Zhao,
• Abstract summary: We introduce a novel strategy employing an adaptive genetic algorithm (GA) for iterative optimization of control sequences to generate quantum nonclassical states. Inspired by Darwinian evolution, the algorithm iteratively refines control sequences using crossover, mutation, and elimination strategies. Our approach, compared to constant control schemes, yields a variety of control sequences capable of maintaining squeezing for the collective spin model.
• Score: 9.168152138847445
• License:
• Abstract: We introduce a novel strategy employing an adaptive genetic algorithm (GA) for iterative optimization of control sequences to generate quantum nonclassical states. Its efficacy is demonstrated by preparing spin-squeezed states in an open collective spin model governed by a linear control field. Inspired by Darwinian evolution, the algorithm iteratively refines control sequences using crossover, mutation, and elimination strategies, starting from a coherent spin state within a dissipative and dephasing environment. An adaptive parameter adjustment mechanism further enhances optimization. Our approach, compared to constant control schemes, yields a variety of control sequences capable of maintaining squeezing for the collective spin model. Furthermore, the proposed strategy exhibits increased effectiveness in diverse systems, while reservoir thermal excitations are shown to negatively impact control outcomes. We discuss feasible experimental implementations and potential extensions to alternative quantum systems, and the adaptability of the GA module. This research establishes the foundation for utilizing GA-like strategies in controlling quantum systems and achieving desired nonclassical states.

Related papers

• A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning [0.0]
  We propose a scheme leveraging reinforcement learning to engineer control fields for generating non-classical states. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state. This research paves the way for its application in manipulating other quantum systems.
  arXiv  Detail & Related papers  (2024-01-29T17:23:25Z)
• Continuous optimization by quantum adaptive distribution search [0.7332146059733189]
  We introduce the quantum adaptive distribution search (QuADS) QuADS integrates Grover adaptive search (GAS) with the covariance matrix adaptation - evolution strategy (CMA-ES) numerical experiments show that QuADS outperforms both GAS and CMA-ES.
  arXiv  Detail & Related papers  (2023-11-29T04:48:09Z)
• 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)
• 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)
• Exploring the limits of the generation of non-classical states of spins coupled to a cavity by optimal control [0.0]
  We investigate the generation of non-classical states of spins coupled to a common cavity by means of a collective driving of the spins. We propose a control strategy using specifically designed series of short coherent and squeezing pulses.
  arXiv  Detail & Related papers  (2021-07-16T16:16:18Z)
• Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model [24.030426634281643]
  In continuous control tasks, widely used policies with Gaussian distributions results in ineffective exploration of environments. We propose a density-free off-policy algorithm, Generative Actor-Critic, using the push-forward model to increase the expressiveness of policies. We show that push-forward policies possess desirable features, such as multi-modality, which can improve the efficiency of exploration and performance of algorithms obviously.
  arXiv  Detail & Related papers  (2021-05-08T16:29:20Z)
• 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)
• 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)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)
• Evolutionary Stochastic Policy Distillation [139.54121001226451]
  We propose a new method called Evolutionary Policy Distillation (ESPD) to solve GCRS tasks. ESPD enables a target policy to learn from a series of its variants through the technique of policy distillation (PD) The experiments based on the MuJoCo control suite show the high learning efficiency of the proposed method.
  arXiv  Detail & Related papers  (2020-04-27T16:19:25Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

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.