Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2402.12361 · Physical Review Applied

Multiaxis quantum noise spectroscopy robust to errors in state preparation and measurement

Muhammad Qasim Khan, Wenzheng Dong, Leigh Norris, Lorenza Viola

Published Aug 29, 2024• 11 citations
arXivHTMLPDF

Abstract

Quantum noise spectroscopy (QNS) is a powerful tool to characterize temporally correlated environmental noise, for noise-tailored control in noisy intermediate-scale quantum processors. However, QNS protocols have been limited by their vulnerability to state-preparation-and-measurement (SPAM) errors, and their inability to simultaneously characterize dephasing and relaxation effects. This work overcomes both of these challenges. The authors present a single-qubit QNS protocol utilizing continuous off-axis control for robust estimation of all multiaxis noise spectra, and show that SPAM errors can significantly alter or mask important features of the underlying native noise.

DOI
10.1103/physrevapplied.22.024074

Related papers

Two-Qubit Spectroscopy of Spatiotemporally Correlated Quantum Noise in Superconducting Qubits

Uwe von Lüpke, Félix Beaudoin, Leigh Norris +9 more

Noise that exhibits significant temporal and spatial correlations across multiple qubits can be especially harmful to both fault-tolerant quantum computation and quantum-enhanced metrology.However, a complete spectral characterization of the noise environment of even a two-qubit system has not been reported thus far.We propose and experimentally demonstrate a protocol for two-qubit dephasing noise spectroscopy based on continuous-control modulation.By combining ideas from spin-locking relaxometry with a statistically motivated robust estimation approach, our protocol allows for the simultaneous reconstruction of all the single-qubit and two-qubit cross-correlation spectra, including access to their distinctive nonclassical features.Only single-qubit control manipulations and state-tomography measurements are employed, with no need for entangled-state preparation or readout of two-qubit observables.While our experimental demonstration uses two superconducting qubits coupled to a shared, colored engineered noise source, our methodology is portable to a variety of dephasing-dominated qubit architectures.By pushing quantum noise spectroscopy beyond the single-qubit setting, our work heralds the characterization of spatiotemporal correlations in both engineered and naturally occurring noise environments.

arXiv:1912.04982•PRX Quantum•Sep 3, 2020•94 citations

Assessing Spatiotemporally Correlated Noise in Superconducting Qubits via Pulse-Based Quantum Noise Spectroscopy

Mayra Amezcua, Leigh Norris, Tom Gilliss +4 more

Spatiotemporally correlated errors are widespread in quantum devices and are particularly adversarial to error correcting schemes. To characterize these errors, we propose and validate a nonparametric quantum noise spectroscopy (QNS) protocol to estimate both spectra and static errors associated with spatiotemporally correlated dephasing noise and fluctuating quantum crosstalk on two qubits. Our scheme reconstructs the real and imaginary components of the two-qubit cross-spectrum by using fixed total time pulse sequences and single qubit and joint two-qubit measurements to separately resolve spatially correlated noise processes. We benchmark our protocol by reconstructing the spectra of spatiotemporally correlated noise processes engineered via the Schrödinger Wave Autoregressive Moving Average technique, emulating dephasing errors. Furthermore, we show that the protocol can outperform existing comb-based QNS protocols. Our results demonstrate the utility of our protocol in characterizing spatiotemporally correlated noise and quantum crosstalk in a multi-qubit device for potential use in noise-adapted control or error protection schemes.

quant-pharXiv:2603.19373•Mar 23, 2026•0 citations

Qubit control noise spectroscopy with optimal suppression of dephasing

Vivian Maloney, Yasuo Oda, Gregory Quiroz +2 more

We extend quantum noise spectroscopy (QNS) of amplitude control noise to settings where dephasing noise or detuning errors make significant contributions to qubit dynamics. Previous approaches to characterize amplitude noise are limited by their vulnerability to low-frequency dephasing noise and static detuning errors, which can overwhelm the target control noise signal and introduce bias into estimates of the amplitude noise spectrum. To overcome this problem, we leverage optimal control to identify a family of amplitude control waveforms that optimally suppress low-frequency dephasing noise and detuning errors, while maintaining the spectral concentration in the amplitude filter essential for spectral estimation. The waveforms found via numerical optimization have surprisingly simple analytic forms, consisting of oscillating sine waves obeying particular amplitude and frequency constraints. In numerically simulated QNS experiments, these waveforms demonstrate superior robustness, enabling accurate estimation of the amplitude noise spectrum in regimes where existing approaches are biased by low-frequency dephasing noise and detuning errors.

arXiv:2204.10894•Physical Review A•Aug 23, 2022•14 citations

Learning Spatiotemporally Correlated Noise in Multi-Qubit Systems with Neural Networks

Abhiram Nallamalli, Shantanu Misra, Gregory Quiroz

Noisy quantum processors require precise and coherent quantum control to effectively execute quantum algorithms. Key to achieving precise control is the characterization of a quantum device to elucidate important details of environmentally-induced and systematic error sources. Quantum noise spectroscopy (QNS) is one such protocol used to characterize spatiotemporally correlated noise. QNS strives to provide estimates of noise statistical properties by using the quantum system as a dynamical probe. Despite its success, QNS can be challenging as it requires model selection prior to protocol implementation and suffers from scalability issues as system size grows. Here, we leverage machine learning to overcome model selection and assist in learning complex noise environments. In particular, we utilize physics-informed neural networks based on the “gray box” model to learn the dynamics of a two-qubit system subject to spatiotemporally correlated noise. Using training data sets based on dynamical decoupling, we show that the model is capable of learning the underlying power spectral densities for each qubit and the strength of parasitic qubit-qubit interactions. Furthermore, we show that the model can predict state-dependent behavior for multi-qubit observables. The results of this study further display the potential utility of machine learning to assist in the domain of quantum characterization and control.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•0 citations

Noise Modeling of the IBM Quantum Platform

Yasuo Oda, Omar Shehab, Gregory Quiroz

The influence of noise in quantum dynamics is one of the main factors preventing current quantum processors from performing quantum computations. Errors must be addressed, which requires a thorough understanding of the nature and interplay of different sources of noise. In this work, we propose an effective error model of single-qubit operations on the IBM Quantum Platform (IBMQP), that possesses considerable predictive power. We showcase how Quantum Noise Spectroscopy (QNS) can be used alongside other error characterization techniques to obtain a more complete error model of the system. The model allows for both Markovian and spatio-temporally correlated non-Markovian environmental and control effects. Model parameters are obtained from a small set of characterization experiments. We show that simulations using this error model are capable of accurately predicting new experimental results.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•3 citations

Revealing Noise in Axial Motion Through Quantum Noise Spectroscopy on a Trapped Ion Processor

Vivian Maloney, Matthew Chow, Leigh Norris +7 more

Native noise processes in quantum processors are often difficult to isolate because multiple error mechanisms contribute to the same measured loss of coherence. Here we use dephasing-robust quantum noise spectroscopy to identify and characterize control noise induced by axial motion in an individually-addressed trapped-ion processor. When the ion motion is transverse to the addressing beam, thermal axial motion couples to the beam profile and produces effective amplitude control noise. We show that this noise is governed primarily by the local beam curvature and appears as a low-frequency contribution to the reconstructed control-noise spectrum. By varying the ion position within the beam profile, we separate curvature-dependent axial-motion noise from curvature-independent native control noise and extract motional parameters that are otherwise difficult to access on this platform. We also apply the protocol in parallel to a four-ion register, demonstrating a spectroscopic method for simultaneous characterization of position-dependent control noise across multiple qubits. The results of this study identify beam inflection points as operating regions that suppress axial-motion-induced noise at the cost of reduced Rabi rate, as found in PRX Quantum 3, 010334 (2022).

quant-pharXiv:2608.04089•Aug 6, 2026•0 citations
Quantum noise spectroscopy (QNS) is a powerful tool to characterize temporally correlated environmental noise, for noise-tailored control in noisy intermediate-scale quantum processors. However, QNS protocols have been limited by their vulnerability to state-preparation-and-measurement (SPAM) errors, and their inability to simultaneously characterize dephasing and relaxation effects. This work overcomes both of these challenges. The authors present a single-qubit QNS protocol utilizing continuous off-axis control for robust estimation of all multiaxis noise spectra, and show that SPAM errors can significantly alter or mask important features of the underlying native noise.
Noise that exhibits significant temporal and spatial correlations across multiple qubits can be especially harmful to both fault-tolerant quantum computation and quantum-enhanced metrology.However, a complete spectral characterization of the noise environment of even a two-qubit system has not been reported thus far.We propose and experimentally demonstrate a protocol for two-qubit dephasing noise spectroscopy based on continuous-control modulation.By combining ideas from spin-locking relaxometry with a statistically motivated robust estimation approach, our protocol allows for the simultaneous reconstruction of all the single-qubit and two-qubit cross-correlation spectra, including access to their distinctive nonclassical features.Only single-qubit control manipulations and state-tomography measurements are employed, with no need for entangled-state preparation or readout of two-qubit observables.While our experimental demonstration uses two superconducting qubits coupled to a shared, colored engineered noise source, our methodology is portable to a variety of dephasing-dominated qubit architectures.By pushing quantum noise spectroscopy beyond the single-qubit setting, our work heralds the characterization of spatiotemporal correlations in both engineered and naturally occurring noise environments.
Spatiotemporally correlated errors are widespread in quantum devices and are particularly adversarial to error correcting schemes. To characterize these errors, we propose and validate a nonparametric quantum noise spectroscopy (QNS) protocol to estimate both spectra and static errors associated with spatiotemporally correlated dephasing noise and fluctuating quantum crosstalk on two qubits. Our scheme reconstructs the real and imaginary components of the two-qubit cross-spectrum by using fixed total time pulse sequences and single qubit and joint two-qubit measurements to separately resolve spatially correlated noise processes. We benchmark our protocol by reconstructing the spectra of spatiotemporally correlated noise processes engineered via the Schrödinger Wave Autoregressive Moving Average technique, emulating dephasing errors. Furthermore, we show that the protocol can outperform existing comb-based QNS protocols. Our results demonstrate the utility of our protocol in characterizing spatiotemporally correlated noise and quantum crosstalk in a multi-qubit device for potential use in noise-adapted control or error protection schemes.
We extend quantum noise spectroscopy (QNS) of amplitude control noise to settings where dephasing noise or detuning errors make significant contributions to qubit dynamics. Previous approaches to characterize amplitude noise are limited by their vulnerability to low-frequency dephasing noise and static detuning errors, which can overwhelm the target control noise signal and introduce bias into estimates of the amplitude noise spectrum. To overcome this problem, we leverage optimal control to identify a family of amplitude control waveforms that optimally suppress low-frequency dephasing noise and detuning errors, while maintaining the spectral concentration in the amplitude filter essential for spectral estimation. The waveforms found via numerical optimization have surprisingly simple analytic forms, consisting of oscillating sine waves obeying particular amplitude and frequency constraints. In numerically simulated QNS experiments, these waveforms demonstrate superior robustness, enabling accurate estimation of the amplitude noise spectrum in regimes where existing approaches are biased by low-frequency dephasing noise and detuning errors.
Noisy quantum processors require precise and coherent quantum control to effectively execute quantum algorithms. Key to achieving precise control is the characterization of a quantum device to elucidate important details of environmentally-induced and systematic error sources. Quantum noise spectroscopy (QNS) is one such protocol used to characterize spatiotemporally correlated noise. QNS strives to provide estimates of noise statistical properties by using the quantum system as a dynamical probe. Despite its success, QNS can be challenging as it requires model selection prior to protocol implementation and suffers from scalability issues as system size grows. Here, we leverage machine learning to overcome model selection and assist in learning complex noise environments. In particular, we utilize physics-informed neural networks based on the “gray box” model to learn the dynamics of a two-qubit system subject to spatiotemporally correlated noise. Using training data sets based on dynamical decoupling, we show that the model is capable of learning the underlying power spectral densities for each qubit and the strength of parasitic qubit-qubit interactions. Furthermore, we show that the model can predict state-dependent behavior for multi-qubit observables. The results of this study further display the potential utility of machine learning to assist in the domain of quantum characterization and control.
The influence of noise in quantum dynamics is one of the main factors preventing current quantum processors from performing quantum computations. Errors must be addressed, which requires a thorough understanding of the nature and interplay of different sources of noise. In this work, we propose an effective error model of single-qubit operations on the IBM Quantum Platform (IBMQP), that possesses considerable predictive power. We showcase how Quantum Noise Spectroscopy (QNS) can be used alongside other error characterization techniques to obtain a more complete error model of the system. The model allows for both Markovian and spatio-temporally correlated non-Markovian environmental and control effects. Model parameters are obtained from a small set of characterization experiments. We show that simulations using this error model are capable of accurately predicting new experimental results.
Native noise processes in quantum processors are often difficult to isolate because multiple error mechanisms contribute to the same measured loss of coherence. Here we use dephasing-robust quantum noise spectroscopy to identify and characterize control noise induced by axial motion in an individually-addressed trapped-ion processor. When the ion motion is transverse to the addressing beam, thermal axial motion couples to the beam profile and produces effective amplitude control noise. We show that this noise is governed primarily by the local beam curvature and appears as a low-frequency contribution to the reconstructed control-noise spectrum. By varying the ion position within the beam profile, we separate curvature-dependent axial-motion noise from curvature-independent native control noise and extract motional parameters that are otherwise difficult to access on this platform. We also apply the protocol in parallel to a four-ion register, demonstrating a spectroscopic method for simultaneous characterization of position-dependent control noise across multiple qubits. The results of this study identify beam inflection points as operating regions that suppress axial-motion-induced noise at the cost of reduced Rabi rate, as found in PRX Quantum 3, 010334 (2022).