Qubit noise deconvolution

• URL: http://arxiv.org/abs/2112.03043v2
• Date: Fri, 28 Jan 2022 08:41:15 GMT
• Title: Qubit noise deconvolution
• Authors: Stefano Mangini, Lorenzo Maccone, Chiara Macchiavello
• Abstract summary: We present a noise deconvolution technique to remove a wide class of noises when performing arbitrary measurements on qubit systems. We exploit it at the data processing step to obtain noise-free estimates of observables evaluated on a qubit system subject to known noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a noise deconvolution technique to remove a wide class of noises when performing arbitrary measurements on qubit systems. In particular, we derive the inverse map of the most common single qubit noisy channels and exploit it at the data processing step to obtain noise-free estimates of observables evaluated on a qubit system subject to known noise. We illustrate a self-consistency check to ensure that the noise characterization is accurate providing simulation results for the deconvolution of a generic Pauli channel, as well as experimental evidence of the deconvolution of decoherence noise occurring on Rigetti quantum hardware.

Related papers

• Bayesian Inference of General Noise Model Parameters from Surface Code's Syndrome Statistics [0.0]
  We propose general noise model Bayesian inference methods that integrate the surface code's tensor network simulator. For stationary noise, where the noise parameters are constant and do not change, we propose a method based on the Markov chain Monte Carlo. For time-varying noise, which is a more realistic situation, we introduce another method based on the sequential Monte Carlo.
  arXiv  Detail & Related papers  (2024-06-13T10:26:04Z)
• Some aspects of noise in binary classification with quantum circuits [2.2311710049695446]
  We study the effects of a restricted single-qubit noise model inspired by real quantum hardware on the performance of binary classification using quantum circuits. We show that noise in the data can work as a regularizer, implying potential benefits from the noise in certain cases for machine learning problems.
  arXiv  Detail & Related papers  (2022-11-11T22:06:17Z)
• Multi-qubit noise deconvolution and characterization [0.0]
  We present a noise deconvolution technique for obtaining noiseless expectation values of noisy observables at the output of n-qubits quantum channels. Our protocol applies to any noise model and for any number of qubits, also in presence of correlations.
  arXiv  Detail & Related papers  (2022-07-25T17:53:36Z)
• High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators: Symmetry Breaking and Floquet Protection [55.41644538483948]
  We study the qubit dephasing caused by the non-Gaussian fluctuators. We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
  arXiv  Detail & Related papers  (2022-06-06T18:02:38Z)
• C2N: Practical Generative Noise Modeling for Real-World Denoising [53.96391787869974]
  We introduce a Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using paired examples. We construct the noise generator in C2N accordingly with each component of real-world noise characteristics to express a wide range of noise accurately.
  arXiv  Detail & Related papers  (2022-02-19T05:53:46Z)
• Learning Noise via Dynamical Decoupling of Entangled Qubits [49.38020717064383]
  Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom. We develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates.
  arXiv  Detail & Related papers  (2022-01-26T20:22:38Z)
• Fingerprints of Qubit Noise in Transient Cavity Transmission [0.0]
  We study a generic two-level system with fluctuating control parameters in a photonic cavity. We find that basic features of the noise spectral density are imprinted in the transient transmission through the cavity. We propose a way of extracting the spectral density for arbitrary noise in a frequency band only bounded by the range of the qubit-cavity detuning.
  arXiv  Detail & Related papers  (2022-01-21T12:37:27Z)
• Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders [62.997667081978825]
  We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
  arXiv  Detail & Related papers  (2021-09-18T14:51:24Z)
• Mitigating depolarizing noise on quantum computers with noise-estimation circuits [1.3375143521862154]
  We present a method to mitigate the depolarizing noise by first estimating its rate with a noise-estimation circuit. We find that our approach in combination with readout-error correction, compiling, randomized, and zero-noise extrapolation produces results close to exact results even for circuits containing hundreds of CNOT gates.
  arXiv  Detail & Related papers  (2021-03-15T17:59:06Z)
• Adaptive noise imitation for image denoising [58.21456707617451]
  We develop a new textbfadaptive noise imitation (ADANI) algorithm that can synthesize noisy data from naturally noisy images. To produce realistic noise, a noise generator takes unpaired noisy/clean images as input, where the noisy image is a guide for noise generation. Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.
  arXiv  Detail & Related papers  (2020-11-30T02:49:36Z)
• A deep learning model for noise prediction on near-term quantum devices [137.6408511310322]
  We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
  arXiv  Detail & Related papers  (2020-05-21T17:47:29Z)

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.