Machine Learning-Enhanced Characterisation of Structured Spectral Densities: Leveraging the Reaction Coordinate Mapping

• URL: http://arxiv.org/abs/2501.07485v1
• Date: Mon, 13 Jan 2025 17:02:04 GMT
• Title: Machine Learning-Enhanced Characterisation of Structured Spectral Densities: Leveraging the Reaction Coordinate Mapping
• Authors: Jessica Barr, Alessandro Ferraro, Mauro Paternostro, Giorgio Zicari,
• Abstract summary: Spectral densities encode essential information about system-environment interactions in open-quantum systems. We leverage machine learning techniques to reconstruct key environmental features using the reaction coordinate mapping. For a dissipative spin-boson model with a structured spectral density expressed as a sum of Lorentzian peaks, we demonstrate that the time evolution of a system observable can be used by a neural network to classify the spectral density as comprising one, two, or three Lorentzian peaks.
• Score: 41.94295877935867
• License:
• Abstract: Spectral densities encode essential information about system-environment interactions in open-quantum systems, playing a pivotal role in shaping the system's dynamics. In this work, we leverage machine learning techniques to reconstruct key environmental features, going beyond the weak-coupling regime by simulating the system's dynamics using the reaction coordinate mapping. For a dissipative spin-boson model with a structured spectral density expressed as a sum of Lorentzian peaks, we demonstrate that the time evolution of a system observable can be used by a neural network to classify the spectral density as comprising one, two, or three Lorentzian peaks and accurately predict their central frequency.

Related papers

• Point-Calibrated Spectral Neural Operators [54.13671100638092]
  We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis. Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)
• Approaching Deep Learning through the Spectral Dynamics of Weights [41.948042468042374]
  spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to clarify and unify several phenomena in deep learning. We identify a consistent bias in optimization across various experiments, from small-scale grokking'' to large-scale tasks like image classification with ConvNets, image generation with UNets, speech recognition with LSTMs, and language modeling with Transformers.
  arXiv  Detail & Related papers  (2024-08-21T17:48:01Z)
• Spectral Density Classification For Environment Spectroscopy [44.99833362998488]
  We leverage the potential of machine learning techniques to reconstruct the features of the environment. For relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic.
  arXiv  Detail & Related papers  (2023-08-01T20:42:59Z)
• Learning dynamical systems: an example from open quantum system dynamics [0.0]
  We will study the dynamics of a small spin chain coupled with dephasing gates. We show how Koopman operator learning is an approach to efficiently learn not only the evolution of the density matrix, but also of every physical observable associated to the system.
  arXiv  Detail & Related papers  (2022-11-12T14:36:13Z)
• Spreading of a local excitation in a Quantum Hierarchical Model [62.997667081978825]
  We study the dynamics of the quantum Dyson hierarchical model in its paramagnetic phase. An initial state made by a local excitation of the paramagnetic ground state is considered. A localization mechanism is found and the excitation remains close to its initial position at arbitrary times.
  arXiv  Detail & Related papers  (2022-07-14T10:05:20Z)
• Collisional open quantum dynamics with a generally correlated environment: Exact solvability in tensor networks [0.0]
  We find a natural Markovian embedding for the system dynamics, where the role of an auxiliary system is played by virtual indices of the network. The results advance tensor-network methods in the fields of quantum optics and quantum transport.
  arXiv  Detail & Related papers  (2022-02-09T19:48:17Z)
• Neural-Network Quantum States for Periodic Systems in Continuous Space [66.03977113919439]
  We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity. For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles. In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
  arXiv  Detail & Related papers  (2021-12-22T15:27:30Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• Pseudomode description of general open quantum system dynamics: non-perturbative master equation for the spin-boson model [0.0]
  We outline a non-perturbative approach for simulating the behavior of open quantum systems interacting with a bosonic environment. Our framework can be used as a powerful and versatile tool for analyzing non-Markovian open system dynamics.
  arXiv  Detail & Related papers  (2021-08-12T13:49:22Z)

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.