Deep learning enhanced noise spectroscopy of a spin qubit environment

• URL: http://arxiv.org/abs/2301.05079v2
• Date: Wed, 10 May 2023 07:52:25 GMT
• Title: Deep learning enhanced noise spectroscopy of a spin qubit environment
• Authors: Stefano Martina, Santiago Hern\'andez-G\'omez, Stefano Gherardini, Filippo Caruso, Nicole Fabbri
• Abstract summary: We experimentally show that the use of neural networks can highly increase the accuracy of noise spectroscopy. Deep learning models can be more accurate than standard DD noise-spectroscopy techniques.
• Score: 5.186945902380689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content of the noise induced by the environment is crucial to protect qubit coherence and optimize its employment in quantum device applications. We experimentally show that the use of neural networks can highly increase the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy (NV) center in diamond. Neural networks are trained over spin coherence functions of the NV center subjected to different Carr-Purcell sequences, typically used for dynamical decoupling (DD). As a result, we determine that deep learning models can be more accurate than standard DD noise-spectroscopy techniques, by requiring at the same time a much smaller number of DD sequences.

Related papers

• How to harness high-dimensional temporal entanglement, using limited interferometry setups [62.997667081978825]
  We develop the first complete analysis of high-dimensional entanglement in the polarization-time-domain. We show how to efficiently certify relevant density matrix elements and security parameters for Quantum Key Distribution. We propose a novel setup that can further enhance the noise resistance of free-space quantum communication.
  arXiv  Detail & Related papers  (2023-08-08T17:44:43Z)
• Digital noise spectroscopy with a quantum sensor [57.53000001488777]
  We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process. Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters. We experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond.
  arXiv  Detail & Related papers  (2022-12-19T02:19:35Z)
• All-Optical Nuclear Quantum Sensing using Nitrogen-Vacancy Centers in Diamond [52.77024349608834]
  Microwave or radio-frequency driving poses a significant limitation for miniaturization, energy-efficiency and non-invasiveness of quantum sensors. We overcome this limitation by demonstrating a purely optical approach to coherent quantum sensing. Our results pave the way for highly compact quantum sensors to be employed for magnetometry or gyroscopy applications.
  arXiv  Detail & Related papers  (2022-12-14T08:34:11Z)
• Nanoscale covariance magnetometry with diamond quantum sensors [0.0]
  Nitrogen vacancy (NV) centers in diamond are atom-scale defects with long spin coherence times. In averaging over many single-NV center experiments, both techniques discard information. Here we propose and implement a new sensing modality, whereby two or more NV centers are measured simultaneously.
  arXiv  Detail & Related papers  (2022-09-19T01:46:19Z)
• Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise [52.77024349608834]
  We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field. These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
  arXiv  Detail & Related papers  (2022-08-01T19:18:26Z)
• Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks [72.09574528342732]
  Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs) They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
  arXiv  Detail & Related papers  (2022-06-29T19:03:10Z)
• The Spectral Bias of Polynomial Neural Networks [63.27903166253743]
  Polynomial neural networks (PNNs) have been shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a $textitspectral bias$ towards low-frequency functions, which yields faster learning of low-frequency components during training. Inspired by such studies, we conduct a spectral analysis of the Tangent Kernel (NTK) of PNNs. We find that the $Pi$-Net family, i.e., a recently proposed parametrization of PNNs, speeds up the
  arXiv  Detail & Related papers  (2022-02-27T23:12:43Z)
• All-optical noise spectroscopy of a solid-state spin [0.0]
  Noise spectroscopy elucidates the fundamental noise sources in spin systems. Existing techniques for noise spectroscopy that rely on microwave fields become infeasible when the microwave power is too weak to generate Rabi rotations of the spin. Here, we demonstrate an alternative all-optical approach to performing noise spectroscopy.
  arXiv  Detail & Related papers  (2021-09-08T02:47:15Z)
• Noise-robust classification of single-shot electron spin readouts using a deep neural network [2.9123559461323016]
  Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. We present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN) In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN)
  arXiv  Detail & Related papers  (2020-12-20T04:26:40Z)
• Resonant Excitation and Purcell Enhancement of Coherent Nitrogen-Vacancy Centers Coupled to a Fabry-P\'{e}rot Micro-Cavity [0.0]
  nitrogen-vacancy (NV) center in diamond has been established as a prime building block for quantum networks. Poor optical coherence of near-surface NV centers has so far prevented their resonant optical control, as would be required for entanglement generation. We demonstrate resonant addressing of individual, fiber-cavity-coupled NV centers, and collection of their Purcell-enhanced coherent photon emission.
  arXiv  Detail & Related papers  (2020-09-17T10:48:16Z)

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.