Diffusion model for analyzing quantum fingerprints in conductance fluctuation
- URL: http://arxiv.org/abs/2506.08617v1
- Date: Tue, 10 Jun 2025 09:25:06 GMT
- Title: Diffusion model for analyzing quantum fingerprints in conductance fluctuation
- Authors: Naoto Yokoi, Yuki Tanaka, Yukito Nonaka, Shunsuke Daimon, Junji Haruyama, Eiji Saitoh,
- Abstract summary: The model reconstructs impurity arrangements and quantum interference patterns in nanometals by using magnetoconductance data.<n>We visualize the attention weights in the model, which efficiently extract information on the non-local correlation of the electron wave functions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A conditional diffusion model has been developed to analyze intricate conductance fluctuations called universal conductance fluctuations or quantum fingerprints appearing in quantum transport phenomena. The model reconstructs impurity arrangements and quantum interference patterns in nanometals by using magnetoconductance data, providing a novel approach to analyze complex data based on machine learning. In addition, we visualize the attention weights in the model, which efficiently extract information on the non-local correlation of the electron wave functions, and the score functions, which represent the force fields in the wave-function space.
Related papers
- Information Dynamics in Quantum Harmonic Systems: Insights from Toy Models [0.0]
This study explores quantum information dynamics using a toy model of coupled harmonic oscillators.<n>We examine how variations in coupling strength, detuning and external factors, such as a magnetic field, influence information flow and computational metrics.<n>In the context of ion transport, we compare sudden and adiabatic protocols, quantifying their fidelity-complexity through a nonadiabaticity metric.
arXiv Detail & Related papers (2025-01-24T09:47:13Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Subsystem Information Capacity in Random Circuits and Hamiltonian Dynamics [3.6343650965508187]
This study focuses on the effective channels formed by the subsystem of random quantum circuits and quantum Hamiltonian evolution.
We reveal the impact of different initial information encoding schemes on information dynamics including one-to-one, one-to-many, and many-to-many.
arXiv Detail & Related papers (2024-05-08T14:18:36Z) - Demonstration of Lossy Linear Transformations and Two-Photon Interference on a Photonic Chip [78.1768579844556]
We show that engineered loss, using an auxiliary waveguide, allows one to invert the spatial statistics from bunching to antibunching.
We study the photon statistics within the loss-emulating channel and observe photon coincidences, which may provide insights into the design of quantum photonic integrated chips.
arXiv Detail & Related papers (2024-04-09T06:45:46Z) - Quantum electrodynamics of lossy magnetodielectric samples in vacuum: modified Langevin noise formalism [55.2480439325792]
We analytically derive the modified Langevin noise formalism from the established canonical quantization of the electromagnetic field in macroscopic media.
We prove that each of the two field parts can be expressed in term of particular bosonic operators, which in turn diagonalize the electromagnetic Hamiltonian.
arXiv Detail & Related papers (2024-04-07T14:37:04Z) - Quantum Effects on the Synchronization Dynamics of the Kuramoto Model [62.997667081978825]
We show that quantum fluctuations hinder the emergence of synchronization, albeit not entirely suppressing it.
We derive an analytical expression for the critical coupling, highlighting its dependence on the model parameters.
arXiv Detail & Related papers (2023-06-16T16:41:16Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Generalization despite overfitting in quantum machine learning models [0.0]
We provide a characterization of benign overfitting in quantum models.
We show how a class of quantum models exhibits analogous features.
We intuitively explain these features according to the ability of the quantum model to interpolate noisy data with locally "spiky" behavior.
arXiv Detail & Related papers (2022-09-12T18:08:45Z) - Calculating non-linear response functions for multi-dimensional
electronic spectroscopy using dyadic non-Markovian quantum state diffusion [68.8204255655161]
We present a methodology for simulating multi-dimensional electronic spectra of molecular aggregates with coupling electronic excitation to a structured environment.
A crucial aspect of our approach is that we propagate the NMQSD equation in a doubled system Hilbert space but with the same noise.
arXiv Detail & Related papers (2022-07-06T15:30:38Z) - Quantum Floquet engineering with an exactly solvable tight-binding chain
in a cavity [0.0]
We provide an exactly solvable model given by a tight-binding chain coupled to a single cavity mode.
We show that perturbative expansions in the light-matter coupling have to be taken with care and can easily lead to a false superradiant phase.
In addition, we derive analytical expressions for the electronic single-particle spectral function and optical conductivity.
arXiv Detail & Related papers (2021-07-26T14:33:20Z) - Tracing Information Flow from Open Quantum Systems [52.77024349608834]
We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment.
Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
arXiv Detail & Related papers (2021-03-22T16:38:31Z) - Characterizing the dynamical phase diagram of the Dicke model via
classical and quantum probes [0.0]
We study the dynamical phase diagram of the Dicke model in both classical and quantum limits.
Our numerical calculations demonstrate that mean-field features of the dynamics remain valid in the exact quantum dynamics.
Our predictions can be verified in current quantum simulators of the Dicke model including arrays of trapped ions.
arXiv Detail & Related papers (2021-02-03T19:05:56Z)
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.