Sources Of Experimental Noise Research Articles

Multiphysics Missing Data Synthesis: A Machine Learning Approach for Mitigating Data Gaps and Artifacts

Abstract The presence of gaps and spurious nonphysical artifacts in datasets is a nearly ubiquitous problem in many scientific and engineering domains. In the context of multiphysics numerical models, data gaps may arise from lack of coordination between modeling elements and limitations of the discretization and solver schemes employed. In the case of data derived from physical experiments, the limitations of sensing and data acquisition technologies, as well as myriad sources of experimental noise, may result in the generation of data gaps and artifacts. In the present work, we develop and demonstrate a machine learning (ML) meta-framework for repairing such gaps in multiphysics datasets. A unique “cross-training” methodology is used to ensure that the ML models capture the underlying multiphysics of the input datasets, without requiring training on datasets free of gaps/artifacts. The general utility of this approach is demonstrated by the repair of gaps in a multiphysics dataset taken from hypervelocity impact simulations. Subsequently, we examine the problem of removing scan artifacts from X-ray computed microtomographic (XCMT) datasets. A unique experimental methodology for acquiring XCMT data, wherein articles are scanned multiple times under different conditions, enables the ready identification of artifacts, their removal from the datasets, and the filling of the resulting gaps using the ML framework. This work concludes with observations regarding the unique features of the developed methodology, and a discussion of potential future developments and applications for this technology.

Signal-noise metrics for RNA binding protein identification reveal broad spectrum protein-RNA interaction frequencies and dynamics

Recent efforts towards the comprehensive identification of RNA-bound proteomes have revealed a large, surprisingly diverse family of candidate RNA-binding proteins (RBPs). Quantitative metrics for characterization and validation of protein-RNA interactions and their dynamic interactions have, however, proven analytically challenging and prone to error. Here we report a method termed LEAP-RBP (Liquid-Emulsion-Assisted-Purification of RNA-Bound Protein) for the selective, quantitative recovery of UV-crosslinked RNA-protein complexes. By virtue of its high specificity and yield, LEAP-RBP distinguishes RNA-bound and RNA-free protein levels and reveals common sources of experimental noise in RNA-centric RBP enrichment methods. We introduce strategies for accurate RBP identification and signal-based metrics for quantifying protein-RNA complex enrichment, relative RNA occupancy, and method specificity. In this work, the utility of our approach is validated by comprehensive identification of RBPs whose association with mRNA is modulated in response to global mRNA translation state changes and through in-depth benchmark comparisons with current methodologies.

Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

Many-qubit Molmer-Sorensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parameterizes these errors from simple experimental measurements, without free parameters. We validate the model in comparison with experiments that implement sequences of MS interactions on two and three $^{171}$Yb$^+$ ions. The model shows good agreement after several MS interactions as quantified by the reduced chi-squared statistic $\chi^2_\mathrm{red}< 2$. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are $91\%$ and $83\%$ of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.92^{+0.06}_{-0.06}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations the model achieves approximation ratios that are 99$\%$ of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.

The impact of vasomotion on analysis of rodent fMRI data.

Small animal fMRI is an essential part of translational research in the cognitive neurosciences. Due to small dimensions and animal physiology preclinical fMRI is prone to artifacts that may lead to misinterpretation of the data. To reach unbiased translational conclusions, it is, therefore, crucial to identify potential sources of experimental noise and to develop correction methods for contributions that cannot be avoided such as physiological noise. Aim of this study was to assess origin and prevalence of hemodynamic oscillations (HDO) in preclinical fMRI in rat, as well as their impact on data analysis. Following the development of algorithms for HDO detection and suppression, HDO prevalence in fMRI measurements was investigated for different anesthetic regimens, comprising isoflurane and medetomidine, and for both gradient echo and spin echo fMRI sequences. In addition to assessing the effect of vasodilation on HDO, it was studied if HDO have a direct neuronal correlate using local field potential (LFP) recordings. Finally, the impact of HDO on analysis of fMRI data was assessed, studying both the impact on calculation of activation maps as well as the impact on brain network analysis. Overall, 303 fMRI measurements and 32 LFP recordings were performed in 71 rats. In total, 62% of the fMRI measurements showed HDO with a frequency of (0.20 ± 0.02) Hz. This frequent occurrence indicated that HDO cannot be generally neglected in fMRI experiments. Using the developed algorithms, HDO were detected with a specificity of 95%, and removed efficiently from the signal time courses. HDO occurred brain-wide under vasoconstrictive conditions in both small and large blood vessels. Vasodilation immediately interrupted HDO, which, however, returned within 1 h under vasoconstrictive conditions. No direct neuronal correlate of HDO was observed in LFP recordings. HDO significantly impacted analysis of fMRI data, leading to altered cluster sizes and F-values for activated voxels, as well as altered brain networks, when comparing data with and without HDO. We therefore conclude that HDO are caused by vasomotion under certain anesthetic conditions and should be corrected during fMRI data analysis to avoid bias.

Single-photon smFRET. III. Application to pulsed illumination

Förster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single-photon, single-molecule FRET (smFRET) data analysis under pulsed illumination including 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; and 3) taking into account detector noise sources such as cross talk and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion article that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions.

Design and integration of single-qubit rotations and two-qubit gates in silicon above one Kelvin

Spin qubits in quantum dots define an attractive platform for quantum information because of their compatibility with semiconductor manufacturing, their long coherence times, and the ability to operate above one Kelvin. However, despite demonstrations of SWAP oscillations, the integration of this two-qubit gate together with single-qubit control to create a universal gate set as originally proposed for single spins in quantum dots has remained elusive. Here, we show that we can overcome these limitations and execute a multitude of native two-qubit gates, together with single-qubit control, in a single device, reducing the operation overhead to perform quantum algorithms. We demonstrate single-qubit rotations, together with the two-qubit gates CROT, CPHASE, and SWAP, on a silicon double quantum dot. Furthermore, we introduce adiabatic and diabatic composite sequences that allow the execution of CPHASE and SWAP gates on the same device, despite the finite Zeeman energy difference. Both two-qubit gates can be executed in less than 100 ns and, by theoretically analyzing the experimental noise sources, we predict control fidelities exceeding 99%, even for operation above one Kelvin.

Density reconstruction in convergent high-energy-density systems using x-ray radiography and Bayesian inference

X-ray radiography is a technique frequently used to diagnose convergent high-energy-density (HED) systems, such as inertial confinement fusion implosion, and provides unique information that is not available through self-emission measurements. We investigate the scope and limits of that information using a radiography simulation combined with the Bayesian inference workflow. The accuracy of density reconstruction from simulated radiographs of spherical implosions driven with 27 kJ laser energy is assessed, including the increase or decrease in accuracy due to the addition of Lagrangian marker layers, Poisson noise, and improved prior information. This work is the first to present the full uncertainty distributions inferred from radiography analysis in HED systems and demonstrates the importance of constructing the full posterior probability density, as opposed to a point estimate, due to the modal structure of the likelihood surface introduced by typical experimental noise sources. This general methodology can be used both for robust analysis of radiographic data and for an improved design of radiography experiments by modeling the full experimental system.

Performance optimisation of a holographic Fourier domain diffuse correlation spectroscopy instrument.

We have previously demonstrated a novel interferometric multispeckle Fourier domain diffuse correlation spectroscopy system that makes use of holographic camera-based detection, and which is capable of making in vivo pulsatile flow measurements. In this work, we report on a systematic characterisation of the signal-to-noise ratio performance of our system. This includes demonstration and elimination of laser mode hopping, and correction for the instrument’s modulation transfer function to ensure faithful reconstruction of measured intensity profiles. We also demonstrate a singular value decomposition approach to ensure that spatiotemporally correlated experimental noise sources do not limit optimal signal-to-noise ratio performance. Finally, we present a novel multispeckle denoising algorithm that allows our instrument to achieve a signal-to-noise ratio gain that is equal to the square root of the number of detected speckles, whilst detecting up to ∼1290 speckles in parallel. The signal-to-noise ratio gain of 36 that we report is a significant step toward mitigating the trade-off that exists between signal-to-noise ratio and imaging depth in diffuse correlation spectroscopy.

Simulating a measurement-induced phase transition for trapped-ion circuits

The rise of programmable quantum devices has motivated the exploration of circuit models which could realize novel physics. A promising candidate is a class of hybrid circuits, where entangling unitary dynamics compete with disentangling measurements. Novel phase transitions between different entanglement regimes have been identified in their dynamical states, with universal properties hinting at unexplored critical phenomena. Trapped-ion hardware is a leading contender for the experimental realization of such physics, which requires not only traditional two-qubit entangling gates, but also a constant rate of local measurements accurately addressed throughout the circuit. Recent progress in engineering high-precision optical addressing of individual ions makes preparing a constant rate of measurements throughout a unitary circuit feasible. Using tensor network simulations, we show that the resulting class of hybrid circuits, prepared with native gates, exhibits a volume-law to area-law transition in the entanglement entropy. This displays universal hallmarks of a measurement-induced phase transition. Our simulations are able to characterize the critical exponents using circuit sizes with tens of qubits and thousands of gates. We argue that this transition should be robust against additional sources of experimental noise expected in modern trapped-ion hardware and will rather be limited by statistical requirements on postselection. Our work highlights the powerful role that tensor network simulations can play in advancing the theoretical and experimental frontiers of critical phenomena.

Phase estimation of time-bin qudits by time-resolved single-photon counting

We present a comprehensive framework for quantum state tomography of time-bin qudits sent through a fiber. Starting from basic assumptions, we define a positive-operator valued measure which is then applied to the quantum state reconstruction problem. A realistic scenario is considered where the time uncertainty of the detector is treated as a source of experimental noise. The performance of the quantum tomography framework is examined through a series of numerical simulations conducted for different parameters describing the apparatus. The quality of state recovery, quantified by the notion of minimum fidelity, is depicted on graphs for a range of fiber lengths. Special attention is paid to relative phase reconstruction for qubits and qutrits. The results present relevant interdependence between the fiber length and the detector jitter.

Experimental and bioinformatics considerations in cancer application of single cell genomics

Single cell genomics offers an unprecedented resolution to interrogate genetic heterogeneity in a patient’s tumour at the intercellular level. However, the DNA yield per cell is insufficient for today’s sequencing library preparation protocols. This necessitates DNA amplification which is a key source of experimental noise. We provide an evaluation of two protocols using micro-fluidics based amplification for whole exome sequencing, which is an experimental scenario commonly used in single cell genomics. The results highlight their respective biases and relative strengths in identification of single nucleotide variations. Towards this end, we introduce a workflow SoVaTSiC, which allows for quality evaluation and somatic variant identification of single cell data. As proof of concept, the framework was applied to study a lung adenocarcinoma tumour. The analysis provides insights into tumour phylogeny by identifying key mutational events in lung adenocarcinoma evolution. The consequence of this inference is supported by the histology of the tumour and demonstrates usefulness of the approach.

Ultra Stable Molecular Sensors by Submicron Referencing and Why They Should Be Interrogated by Optical Diffraction-Part II. Experimental Demonstration.

Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental noise sources. In this paper, we demonstrate that sensors that apply the spatial affinity lock-in principle (part I) and perform readout by diffraction overcome the drawbacks of established refractometric biosensors. We show this with a direct comparison of the cover refractive index jump sensitivity as well as the surface mass resolution of an unstabilized diffractometric biosensor with a state-of-the-art Biacore 8k. A combined refractometric diffractometric biosensor demonstrates that a refractometric sensor requires a much higher measurement precision than the diffractometric to achieve the same resolution. In a conceptual and quantitative discussion, we elucidate the physical reasons behind and define the figure of merit of diffractometric biosensors. Because low-precision unstabilized diffractometric devices achieve the same resolution as bulky stabilized refractometric sensors, we believe that label-free optical sensors might soon move beyond the drug discovery lab as miniaturized, mass-produced environmental/medical sensors. In fact, combined with the right surface chemistry and recognition element, they might even bring the senses of smell/taste to our smart devices.

Robust Spin Squeezing via Photon-Mediated Interactions on an Optical Clock Transition.

Cavity QED is a promising avenue for the deterministic generation of entangled and spin-squeezed states for quantum metrology. One archetypal scheme generates squeezing via collective one-axis twisting interactions. However, we show that in implementations using optical transitions in long-lived atoms the achievable squeezing is fundamentally limited by collectively enhanced emission into the cavity mode which is generated in parallel with the cavity-mediated spin-spin interactions. We propose an alternative scheme which generates a squeezed state that is protected from collective emission, and investigate its sensitivity to realistic sources of experimental noise and imperfections.

Variational Algorithms for Analyzing Noisy Multistate Diffusion Trajectories

Single-particle tracking offers a noninvasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise sources are needed to realize the full quantitative potential of the method. We report algorithms for hidden Markov-based analysis of single-particle tracking data, which incorporate most sources of experimental noise, including heterogeneous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speedups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single-particle tracking data.

Sample and substrate preparation for exploring living neurons in culture with quantitative-phase imaging.

Quantitative-phase imaging (QPI) has recently emerged as a powerful new quantitative microscopy technique suitable for the noninvasive exploration of the structure and dynamics of transparent specimens, including living cells in culture. Indeed, the quantitative-phase signal (QPS), induced by transparent living cells, can be detected with a nanometric axial sensitivity, and contains a wealth of information about both cell morphology and content. However, QPS is also sensitive to various sources of experimental noise. In this chapter, we emphasize how to properly and specifically measure the cell-mediated QPS in a wet-lab environment, when measuring with a digital holographic microscope (DHM). First, we present the substrate-requisite characteristics for properly achieving such cell-mediated QPS measurements at single-cell level. Then, we describe how quantitative-phase digital holographic microscopy (QP-DHM) can be used to numerically process holograms and subsequently reshape wavefronts in association with post-processing algorithms, thereby allowing for highly stable QPS obtainable over extended periods of time. Such stable QPS is a prerequisite for exploring the dynamics of specific cellular processes. We also describe experimental procedures that make it possible to extract important biophysical cellular parameters from QPS including absolute cell volume, transmembrane water permeability, and the movements of water in and out of the cell. To illustrate how QP-DHM can reveal the dynamics of specific cellular processes, we show how the monitoring of transmembrane water movements can be used to resolve the neuronal network dynamics at single-cell level. This is possible because QPS can measure the activity of electroneutral cotransports, including NKCC1 and KCC2, during a neuronal firing mediated by glutamate, the main excitatory neurotransmitter in the brain. Finally, we added a supplemental section, with more technical details, for readers who are interested in troubleshooting live-cell QP-DHM.

Batch effects and the effective design of single-cell gene expression studies

Single-cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single-cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.

Simulating the Haldane phase in trapped-ion spins using optical fields

We propose to experimentally explore the Haldane phase in spin-one XXZ antiferromagnetic chains using trapped ions. We show how to adiabatically prepare the ground states of the Haldane phase, demonstrate their robustness against sources of experimental noise, and propose ways to detect the Haldane ground states based on their excitation gap and exponentially decaying correlations, nonvanishing nonlocal string order, and doubly-degenerate entanglement spectrum.

Optimal State Estimation for Cavity Optomechanical Systems.

We demonstrate optimal state estimation for a cavity optomechanical system through Kalman filtering. By taking into account nontrivial experimental noise sources, such as colored laser noise and spurious mechanical modes, we implement a realistic state-space model. This allows us to obtain the conditional system state, i.e., conditioned on previous measurements, with a minimal least-squares estimation error. We apply this method to estimate the mechanical state, as well as optomechanical correlations both in the weak and strong coupling regime. The application of the Kalman filter is an important next step for achieving real-time optimal (classical and quantum) control of cavity optomechanical systems.

Time-domain reflectance diffuse optical tomography with Mellin-Laplace transform for experimental detection and depth localization of a single absorbing inclusion

We show how to apply the Mellin-Laplace transform to process time-resolved reflectance measurements for diffuse optical tomography. We illustrate this method on simulated signals incorporating the main sources of experimental noise and suggest how to fine-tune the method in order to detect the deepest absorbing inclusions and optimize their localization in depth, depending on the dynamic range of the measurement. To finish, we apply this method to measurements acquired with a setup including a femtosecond laser, photomultipliers and a time-correlated single photon counting board. Simulations and experiments are illustrated for a probe featuring the interfiber distance of 1.5 cm and show the potential of time-resolved techniques for imaging absorption contrast in depth with this geometry.

Simple scheme for multiqubit quantum controlled phase-flip gate with singlet-triplet spin qubits in nanowire double quantum dots

Abstract We propose an alternative scheme for one-step implementing three-qubit controlled phase-flip gate of spin qubits in spatially separated double quantum dots with a superconducting stripline resonator (SSR). Due to the switchable coupling effect between the double quantum dots and SSR, the present scheme could be easily generalized for multi-qubits. The feasibility of the present scheme is characterized by exact numerical simulations that incorporate various sources of experimental noise. Our results show the possibility to realize this proposed scheme within current experimental technologies.