Noise-tailored Constructions for Spin Wigner Function Kernels
- URL: http://arxiv.org/abs/2310.15855v1
- Date: Tue, 24 Oct 2023 14:13:04 GMT
- Title: Noise-tailored Constructions for Spin Wigner Function Kernels
- Authors: Michael Hanks and Soovin Lee and M. S. Kim
- Abstract summary: Noisey quantum channels and the inference of their effects on general observables are challenging problems.
Here, we investigate spin Wigner functions for multi-qudit systems.
We capture the effects of several probabilistic unitary noise models in few parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The effective use of noisy intermediate-scale quantum devices requires error
mitigation to improve the accuracy of sampled measurement distributions. The
more accurately the effects of noise on these distributions can be modeled, the
more closely error mitigation will be able to approach theoretical bounds. The
characterisation of noisy quantum channels and the inference of their effects
on general observables are challenging problems, but in many cases a change in
representation can greatly simplify the analysis. Here, we investigate spin
Wigner functions for multi-qudit systems. We generalise previous kernel
constructions, capturing the effects of several probabilistic unitary noise
models in few parameters.
Related papers
- Error mitigation with stabilized noise in superconducting quantum processors [2.2752198833969315]
We experimentally demonstrate that tuning of the qubit-TLS interactions helps reduce noise instabilities and enables more reliable error-mitigation performance.
We anticipate that the capabilities introduced here will be crucial for the exploration of quantum applications on solid-state processors at non-trivial scales.
arXiv Detail & Related papers (2024-07-02T17:47:07Z) - Benchmarking bosonic modes for quantum information with randomized displacements [0.0]
We show a bosonic randomized benchmarking protocol that uses randomized displacements of bosonic modes in phase space to determine their quality.
We experimentally validate the analytical models by injecting engineered noise into the motional mode of a trapped ion system.
Finally, we investigate the intrinsic error properties in our system, identifying the presence of highly correlated dephasing noise as the dominant process.
arXiv Detail & Related papers (2024-05-24T06:00:05Z) - Variational waveguide QED simulators [58.720142291102135]
Waveguide QED simulators are made by quantum emitters interacting with one-dimensional photonic band-gap materials.
Here, we demonstrate how these interactions can be a resource to develop more efficient variational quantum algorithms.
arXiv Detail & Related papers (2023-02-03T18:55:08Z) - 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) - Noisy Quantum Kernel Machines [58.09028887465797]
An emerging class of quantum learning machines is that based on the paradigm of quantum kernels.
We study how dissipation and decoherence affect their performance.
We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
arXiv Detail & Related papers (2022-04-26T09:52:02Z) - Probabilistic error cancellation with sparse Pauli-Lindblad models on
noisy quantum processors [0.7299729677753102]
We present a protocol for learning and inverting a sparse noise model that is able to capture correlated noise and scales to large quantum devices.
These advances allow us to demonstrate PEC on a superconducting quantum processor with crosstalk errors.
arXiv Detail & Related papers (2022-01-24T18:40:43Z) - Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and
Cycle Benchmarking [50.8020641352841]
We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application.
We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor.
This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
arXiv Detail & Related papers (2022-01-08T23:12:55Z) - Probe incompatibility in multiparameter noisy quantum metrology [0.0]
We study the issue of the optimal probe incompatibility in the simultaneous estimation of multiple parameters in generic noisy channels.
In particular, we show that in lossy multiple arm interferometry the probe incompatibility is as strong as in the noiseless scenario.
We introduce the concept of emphrandom quantum sensing and show how the tools developed may be applied to multiple channel discrimination problems.
arXiv Detail & Related papers (2021-04-22T18:03:16Z) - Noise Estimation for Generative Diffusion Models [91.22679787578438]
In this work, we present a simple and versatile learning scheme that can adjust the noise parameters for any given number of steps.
Our approach comes at a negligible computation cost.
arXiv Detail & Related papers (2021-04-06T15:46:16Z) - Shape Matters: Understanding the Implicit Bias of the Noise Covariance [76.54300276636982]
Noise in gradient descent provides a crucial implicit regularization effect for training over parameterized models.
We show that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise.
Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not.
arXiv Detail & Related papers (2020-06-15T18:31:02Z) - Noise-adaptive test of quantum correlations with quasiprobability
functions [0.0]
We introduce a method for testing quantum correlations in terms of quasiprobability functions in the presence of noise.
We analyze the effects of measurement imperfection and thermal environment on quantum correlations and show that their noise effects can be well encapsulated into the change of the order parameter of the generalized quasiprobability function.
arXiv Detail & Related papers (2020-02-14T01:51:29Z)
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.