Using deep learning to understand and mitigate the qubit noise environment

• URL: http://arxiv.org/abs/2005.01144v2
• Date: Mon, 11 Jan 2021 10:40:09 GMT
• Title: Using deep learning to understand and mitigate the qubit noise environment
• Authors: David F. Wise, John J. L. Morton, and Siddharth Dhomkar
• Abstract summary: We propose to address the challenge of extracting accurate noise spectra from time-dynamics measurements on qubits. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath. Our results can be applied to a wide range of qubit platforms and provide a framework for improving qubit performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance.

Related papers

• Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems [0.6138671548064356]
  We show how to achieve higher-order spectral estimation for noise-optimized circuit design. Remarkably, we find that the digitally driven qubit dynamics can be solely determined by the complexity of the applied control.
  arXiv  Detail & Related papers  (2025-02-08T02:09:40Z)
• Expedited Noise Spectroscopy of Transmon Qubits [1.5097076557887232]
  Noise continues to pose a crucial challenge when it comes to scaling up present day quantum processors. We use a machine learning-based methodology to quickly extract noise spectra of multiple qubits. We validate our protocol using superconducting qubits available on the IBM Quantum platform.
  arXiv  Detail & Related papers  (2025-02-02T05:52:45Z)
• Enhance Vision-Language Alignment with Noise [59.2608298578913]
  We investigate whether the frozen model can be fine-tuned by customized noise. We propose Positive-incentive Noise (PiNI) which can fine-tune CLIP via injecting noise into both visual and text encoders.
  arXiv  Detail & Related papers  (2024-12-14T12:58:15Z)
• Optimal adaptation of surface-code decoders to local noise [0.0]
  Noise characterization of a quantum device can be used to improve the performance of quantum error-correcting codes. We present a method to determine the maximum extent to which adapting a surface-code decoder to a noise feature can lead to a performance improvement.
  arXiv  Detail & Related papers  (2024-03-13T17:12:33Z)
• Efficient learning of the structure and parameters of local Pauli noise channels [1.5229257192293197]
  We present a novel approach for learning Pauli noise channels over n qubits. We achieve our results by leveraging a groundbreaking result by Bresler for efficiently learning Gibbs measures. Our method is efficient both in the number of samples and postprocessing without giving up on other desirable features.
  arXiv  Detail & Related papers  (2023-07-06T12:42:49Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses [0.20315704654772418]
  Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits. These noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence. While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development.
  arXiv  Detail & Related papers  (2022-05-27T20:35:24Z)
• Learning based signal detection for MIMO systems with unknown noise statistics [84.02122699723536]
  This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Our framework is driven by an unsupervised learning approach, where only the noise samples are required.
  arXiv  Detail & Related papers  (2021-01-21T04:48:15Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)

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.