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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Distributionally Robust Variational Quantum Algorithms With Shifted Noise

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is the high sensitivity of quantum noise to environmental changes, and its propensity to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by real-time noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Shot‐Noise‐Driven Macroscopic Vibrations and Displacement Transduction in Quantum Tunnel Junctions

Back‐action is an inevitable result of quantum measurement. Although the microscopic impacts of shot‐noise back‐action have been explored, macroscopic evidence is seldom documented, especially in the field of electrical transport. Tunneling shot‐noise has been shown to excite the fundamental flexural mode of the host crystal using a sub‐GHz lumped‐element radio‐frequency quantum point contact (QPC). Here, in this study, this aspect of shot‐noise back‐action is examined at much higher operational frequencies and wider bandwidths by employing a GaAs QPC integrated into a planar superconducting cavity within the circuit‐QED framework, leading to the observation of the excitation of multiple mechanical modes. The device operates in the shot‐noise‐limited regime. Constructed on a piezoelectric platform, there is positive feedback between the electrical and mechanical degrees of freedom within the QPC. Shot‐noise excites piezoelectric vibrational modes; concurrently the resulting polarization charges enhance tunneling and develop peaks in the shot‐noise spectra at the modal frequencies. The excitation of vibrational modes is a macroscopic demonstration of measurement back‐action, and the amplitudes of the noise‐peaks enable us to calibrate the displacement sensitivity of the QPC‐resonator systems, which is in the range $≈ 35 fm/√ Hz$, making it an excellent sensor for ultra‐sensitive and rapid strain/displacement detection.

Kumbhakar, Prasanta [Indian Institute of Science E↗
DOE OSTI2025

Spurious solar-wind effects on acceleration noise in LISA Pathfinder

Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA's Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114 d time period from 1 March 2016 to 23 June 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and fast-Fourier transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3 month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the Sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modeling active control of resistive wall mode with power saturation and sensor noise on HL-2M

The resistive wall mode (RWM) control on the HL-2M tokamak is simulated with the MARS-F code (Liu et al 2000 Phys. Plasmas 7 3681), aiming at quantifying control current and voltage requirements when more realistic issues are taken into account, i.e. the control power saturation and the sensor signal noise. The fluid model predicts a narrow stability region for the n = 1 RWM without magnetic feedback, in the 2D parameter space of the plasma pressure versus the toroidal flow speed. Magnetic feedback can fully stabilize the RWM on HL-2M. Without considering the voltage limitation and the sensor signal noise, it is found that plasma flow helps active control of the mode, by reducing the required critical feedback gain for both flux-to-current and flux-to-voltage control schemes. In the absence of the sensor signal noise, the lowest control voltage saturation level, below which the RWM control is lost, is found to roughly satisfy a linear relation to the plasma flow frequency, indicating that subsonic plasma flow is effective in relaxing the control power requirement for the RWM feedback stabilization. The presence of the sensor signal noise substantially modifies the feedback results. A statistical study finds that the sensor signal noise, with the standard deviation of 0.1 G on HL-2M, roughly doubles the required control voltage for successful mode control. The synergistic stabilization effect due to plasma flow is somewhat weakened by the presence of the sensor signal noise. At a given rotation, the tolerable voltage limit generally increases with increasing feedback gain due to the sensor signal noise.

Physics↗
DOE OSTI2026

Noise-canceling quantum feedback: Non-Hermitian dynamics with applications to state preparation and magic state distillation

Time-continuous quantum measurement allows for the tracking of a quantum system in real time via sequences of short, and individually weak, measurement intervals. Such measurements are necessarily invasive, imparting backaction to the system, and allowing the observer to update their state estimate based on stochastic measurement outcomes. Feedback control then involves real-time interventions by an observer, conditioned on the time-continuous measurement signal that they receive. Here, we consider here diffusive quantum trajectories and focus on the “noise-canceling” subset of feedback protocols that aim to minimize the degree of stochasticity in the dynamics. We derive such a class of feedback operations, showing that under the idealized assumptions of pure states, unit measurement efficiency, and zero time delay in implementing feedback operations, perfectly noise-canceling feedback always exists. We consider the resulting noise-canceled dynamics generated by an effective non-Hermitian Hamiltonian; while non-Hermitian Hamiltonians from continuous monitoring generally describe rare dynamics (accessible by costly postselection), the use of noise-canceling feedback here leads to non-Hermitian dynamics that occur deterministically. We demonstrate this via examples of entangled state preparation and stabilization. We then illustrate the potential for the application of noise cancellation to boost success rates in magic state distillation protocols. We show that adding feedback based on noise cancellation into a time-continuous five-to-one distillation protocol leads to higher probabilities of successful distillation across a range of input errors and increases the threshold on input errors for which the protocol is effective. Our results highlight the efficacy of noise-canceling feedback-aided protocols for quantum state preparation and stabilization tasks.

Karmakar, Tathagata [University of California, Ber↗
DOE OSTI2021

Extending quantum probabilistic error cancellation by noise scaling

Here, we propose a general framework for quantum error mitigation that combines and generalizes two techniques: probabilistic error cancellation (PEC) and zero-noise extrapolation (ZNE). Similar to PEC, the proposed method represents ideal operations as linear combinations of noisy operations that are implementable on hardware. However, instead of assuming a fixed level of hardware noise, we extend the set of implementable operations by noise scaling. By construction, this method encompasses both PEC and ZNE as particular cases and allows us to investigate a larger set of hybrid techniques. For example, gate extrapolation can be used to implement PEC without requiring knowledge of the device’s noise model, e.g., avoiding gate-set tomography. Alternatively, probabilistic error reduction can be used to estimate expectation values at intermediate virtual noise strengths (below the hardware level), leading to partially mitigated results at a lower sampling cost. Moreover, multiple results obtained with different noise-reduction factors can be further postprocessed with ZNE to better approximate the zero-noise limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Spatial noise correlations in a Si/SiGe two-qubit device from Bell state coherences

Here, we study spatial noise correlations in a Si/SiGe two-qubit device with integrated micromagnets. Our method relies on the concept of decoherence-free subspaces, whereby we measure the coherence time for two different Bell states, designed to be sensitive only to either correlated or anticorrelated noise, respectively. From these measurements we find weak correlations in low-frequency noise acting on the two qubits, while no correlations could be detected in high-frequency noise. We expect nuclear spin noise to have an uncorrelated nature. A theoretical model and numerical simulations give further insight into the additive effect of multiple independent (anti)correlated noise sources with an asymmetric effect on the two qubits as can result from charge noise. Such a scenario in combination with nuclear spins is plausible given the data and the known decoherence mechanisms. This work is highly relevant for the design of optimized quantum error correction codes for spin qubits in quantum dot arrays, as well as for optimizing the design of future quantum dot arrays.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Effects of noise on the overparametrization of quantum neural networks

Overparametrization is one of the most surprising and notorious phenomena in machine learning. Recently, there have been several efforts to study if, and how, quantum neural networks (QNNs) acting in the absence of hardware noise can be overparametrized. In particular, it has been proposed that a QNN can be defined as overparametrized if it has enough parameters to explore all available directions in state space. That is, if the rank of the quantum Fisher information matrix (QFIM) for the QNN's output state is saturated. Here, we explore how the presence of noise affects the overparametrization phenomenon. Our results show that noise can “turn on” previously zero eigenvalues of the QFIM. This enables the parametrized state to explore directions that were otherwise inaccessible, thus potentially turning an overparametrized QNN into an underparametrized one. For small noise levels, the QNN is quasioverparametrized, as large eigenvalues coexists with small ones. Then, we prove that as the magnitude of noise increases all the eigenvalues of the QFIM become exponentially suppressed, indicating that the state becomes insensitive to any change in the parameters. As such, there is a pull-and-tug effect where noise can enable new directions but also suppress the sensitivity to parameter updates. Finally, our results imply that current QNN capacity measures are ill-defined when hardware noise is present. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2020

Origin and mitigation of wind noise on balloon-borne infrasound microbarometers

High-altitude monitoring of low-frequency acoustic waves (infrasound) on Earth has regained prominence in recent years, primarily driven by improvements in light-weight sensor technology and advances in scientific ballooning techniques. Balloon-borne infrasound monitoring is also being proposed as a remote sensing technique for planetary exploration. Contrary to ground-based infrasound monitoring, the infrasound noise background in the stratosphere as measured by a balloon remains uncharacterized and the efficacy of wind noise mitigation filters has not been investigated. In this study, an analysis of pressure data collected using infrasound microbarometers during the flight of a long-duration zero pressure balloon is presented. Here, a dramatic reduction of background noise in the stratosphere is demonstrated and it is shown that wind noise mitigation filters are not effective at reducing wind noise under these conditions. Results from this study demonstrate stratospheric balloons as a low-noise platform for infrasound monitoring and motivate the development of improved noise mitigation tools.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Improving the performance of cryogenic calorimeters with nonlinear multivariate noise cancellation algorithms

Abstract State-of-the-art physics experiments require high-resolution, low-noise, and low-threshold detectors to achieve competitive scientific results. However, experimental environments invariably introduce sources of noise, such as electrical interference or microphonics. The sources of this environmental noise can often be monitored by adding specially designed “auxiliary devices” (e.g. microphones, accelerometers, seismometers, magnetometers, and antennae). A model can then be constructed to predict the detector noise based on the auxiliary device information, which can then be subtracted from the true detector signal. Here, we present a multivariate noise cancellation algorithm which can be used in a variety of settings to improve the performance of detectors using multiple auxiliary devices. To validate this approach, we apply it to simulated data to remove noise due to electromagnetic interference and microphonic vibrations. We then employ the algorithm to a cryogenic light detector in the laboratory and show an improvement in the detector performance. Finally, we motivate the use of nonlinear terms to better model vibrational contributions to the noise in thermal detectors. We show a further improvement in the performance of a particular channel of the CUORE detector when using the nonlinear algorithm in combination with optimal filtering techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Role of Atmospheric Noise in Decadal SST Variability

Abstract A substantial role for atmospheric noise in simulations of decadal internal variability of SST is demonstrated by a comparison of a multicentury climate model control and a corresponding interactive ensemble (IE) simulation. The IE is designed to reduce atmospheric noise in the heat flux, wind stress, and freshwater flux at the air–sea interface. This comparison suggests that nearly all SST variability on decadal time scales is forced by internal atmospheric variability. The results are examined to determine the relative roles of atmospheric surface heat flux noise and ocean dynamics in the decadal volume-averaged heat budget of the upper ocean. The regional heat budgets in two regions, the South Pacific and the midlatitude North Atlantic, show the net atmospheric surface heat flux to be approximately in equilibrium with the ocean dynamics forcing. The IE and control results are used in the equilibrium heat budget approximation to infer the atmospheric heat flux response to SST, as well as the time series of the control atmospheric noise surface heat flux and ocean dynamics forcings for several regions. The South Pacific region SST is found to be primarily forced by the atmospheric noise surface heat flux and the North Atlantic region SST is forced by the ocean dynamics. Similar strengths for the atmospheric heat flux noise and ocean dynamics forcing, with an interdecadal atmospheric heat flux noise time scale and a centennial ocean dynamics time scale, are found for an Atlantic multidecadal variability region SST.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

Some of the key challenges in Earth system prediction arise from an uncertain representation of unpredictable natural variability, across time and space scales. This variability, hereafter referred to as noise, represents the quantity against which the strength of a signal of interest is measured to assess predictability. Underestimating or overestimating this noise in Earth system models can lead to issues ranging between overconfidence in an erroneous prediction and a lack thereof in an otherwise accurate one. Noise is part of the physical processes in the Earth system and can amplify or damp a signal of interest in a complex manner that is not systematically characterized. Specifically, while there has been significant progress in our understanding of multiscale interactions among known modes of variability (potentially predictable signals), how these are impacted by the noise is not clear. Recent effort toward stochastic parametrization schemes that provide a representation of noise due to uncertain sub-grid processes in climate models (Berner et al., 2017 and references therein) has shown promise not only in reducing biases and improving probabilistic prediction but also in improving the representation of natural variability in the model toward what is observed. However, today’s standard schemes for stochastic parametrization are still simplistic as they do not take the state of the atmosphere into account (other than by the use of multiplicative noise for model tendencies). Furthermore they require hand-tuning and trial-and-error testing for the magnitude of the added noise, which is difficult in a chaotic system that is as complex as the Earth system.

58 GEOSCIENCES↗
DOE OSTI2024

Universal framework for simultaneous tomography of quantum states and SPAM noise

We present a general denoising algorithm for performing simultaneous tomography of quantum states and measurement noise. This algorithm allows us to fully characterize state preparation and measurement (SPAM) errors present in any quantum system. Our method is based on the analysis of the properties of the linear operator space induced by unitary operations. Given any quantum system with a noisy measurement apparatus, our method can output the quantum state and the noise matrix of the detector up to a single gauge degree of freedom. We show that this gauge freedom is unavoidable in the general case, but this degeneracy can be generally broken using prior knowledge on the state or noise properties, thus fixing the gauge for several types of state-noise combinations with no assumptions about noise strength. Such combinations include pure quantum states with arbitrarily correlated errors, and arbitrary states with block independent errors. This framework can further use available prior information about the setting to systematically reduce the number of observations and measurements required for state and noise detection. Our method effectively generalizes existing approaches to the problem, and includes as special cases common settings considered in the literature requiring an uncorrelated or invertible noise matrix, or specific probe states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

JWST Noise Floor. I. Random Error Sources in JWST NIRCam Time Series

James Webb Space Telescope (JWST) transmission and emission spectra will provide invaluable glimpses of transiting exoplanet atmospheres, including possible biosignatures. This promising science from JWST, however, will require exquisite precision and understanding of systematic errors that can impact the time series of planets crossing in front of and behind their host stars. Here, we provide estimates of the random noise sources affecting JWST Near-Infrared Camera (NIRCam) time-series data on the integration-to-integration level. We find that 1/f noise can limit the precision of grism time series for two groups (230–1000 ppm depending on the extraction method and extraction parameters), but will average down like the square root of N frames/reads. The current NIRCam grism time-series mode is especially affected by 1/f noise because its GRISMR dispersion direction is parallel to the detector fast-read direction, but could be alleviated in the GRISMC direction. Care should be taken to include as many frames as possible per visit to reduce this 1/f noise source: thus, we recommend the smallest detector subarray sizes one can tolerate, four output channels, and readout modes that minimize the number of skipped frames (RAPID or BRIGHT2). We also describe a covariance-weighting scheme that can significantly lower the contributions from 1/f noise as compared to sum extraction. We evaluate the noise introduced by pre-amplifier offsets, random telegraph noise, and high dark current resistor capacitor (RC) pixels and find that these are correctable below 10 ppm once background subtraction and pixel masking are performed. We explore systematic error sources in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Development and experimental verification of an active noise cancellation (ANC) method for non-invasive sensing

Ultrasonic wave propagation may be used in non-invasive sensing to identify the properties of a fluid in a container. An ultrasonic excitation can be coupled into the liquid or the gas through the walls of the container and detected by a receiver on the other side. However, in addition to this fluid path, another signal will also propagate through the structure and is referred as noise in this study. For most materials, this structural noise will be of higher amplitude compared to the fluid signal due to the large acoustic impedance difference between the solid container and the fluid. In this paper, an active noise cancellation (ANC) method is developed and demonstrated on an experimental setup. It was shown that this method cancels more than 90% of the noise signal in a given time window of interest. A sequential version of this cancellation method is also proposed, and it was shown that its performance improves as the number of canceling excitations is increased. The performance was found to be independent of the location of canceling transducers and position of the silence window but dependent on the width of the silence window. As an example, the ANC method was applied to noise cancellation of a signal travelling through water in a stainless-steel container. The signal-to-noise ratio (SNR) of the water signal in the window of interest was increased by 148% from 13.98 dB to 34.68 dB by using the proposed ANC method.

42 ENGINEERING↗
DOE OSTI2024

Understanding the Phase of Responsivity and Noise Sources in Frequency-Domain Multiplexed Readout of Transition Edge Sensor Bolometers

Abstract Cosmic microwave background (CMB) experiments have deployed focal planes with $$\mathcal {O}(10^{4})$$ O ( 10 4 ) transition edge sensor (TES) bolometers cooled to sub-Kelvin temperatures by multiplexing the readout of many TES channels onto a single pair of wires. Digital Frequency-domain Multiplexing (DfMux) is a multiplexing technique used in many CMB polarization experiments, such as the Simons Array, SPT-3 G, and EBEX. The DfMux system studied here uses LC filters with resonant frequencies ranging from 1.5 to 4.5 MHz connected to an array of TESs. Each detector has an amplitude-modulated carrier tone at the resonant frequency of its accompanying LC resonator. The signal is recovered via quadrature demodulation where the in-phase (I) component of the demodulated current is in phase with the complex admittance of the circuit and the quadrature (Q) component is orthogonal to I. Observed excess current noise in the Q component is consistent with fluctuations in the resonant frequency. This noise has been shown to be non-orthogonal to the phase of the detector’s responsivity. We present a detailed analysis of the phase of responsivity of the TES and noise sources in our DfMux readout system. Further, we investigate how modifications to the TES operating resistance and bias frequency can affect the phase of noise relative to the phase of the detector responsivity, using data from Simons Array to evaluate our predictions. We find that both the phase of responsivity and phase of noise are functions of the two tuning parameters, which can be purposefully selected to maximize signal-to-noise (SNR) ratio.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗