Fidelity Parameter Research Articles

Relationship between fidelity and spin squeezing parameter in a two-qutrit system under nonlinear Hamiltonians

Relationship between fidelity and spin squeezing parameter in a two-qutrit system under nonlinear Hamiltonians

Decoherence time control by interconnection for finite-level quantum memory systems

This paper is concerned with open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices for finite-level systems. The Hamiltonian and the operators of coupling of the system to the external bosonic fields depend linearly on the system variables. The fields are represented by quantum Wiener processes which drive the system dynamics according to a quasilinear Hudson-Parthasarathy quantum stochastic differential equation whose drift vector and dispersion matrix are affine and linear functions of the system variables. This setting includes the zero-Hamiltonian isolated system dynamics as a particular case, where the system variables are constant in time, which makes them potentially applicable as a quantum memory. In a more realistic case of nonvanishing system-field coupling, we define a memory decoherence time when a mean-square deviation of the system variables from their initial values becomes relatively significant as specified by a weighting matrix and a fidelity parameter. We consider the decoherence time maximization over the energy parameters of the system and obtain a condition under which the zero Hamiltonian provides a suboptimal solution. This optimization problem is also discussed for a direct energy coupling interconnection of such systems.

Leveraging trust for joint multi-objective and multi-fidelity optimization

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. Multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions—up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous or discrete fidelity dimensions, these techniques have wide-ranging applicability in tackling simulation challenges across various scientific computing fields such as plasma physics and fluid dynamics.

Joint MAPLE: Accelerated joint T1 and mapping with scan-specific self-supervised networks.

Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1, , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping. Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition. In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-based estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%. Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.

2-D Quantum Ising Model Fidelity and Order Parameter

This paper explores an interesting relationship between the ground-state fidelity quantum phase transitions and bifurcations per lattice site, and proposes a universal order parameter for the quantum Ising model for a square lattice of infinite-size in two spatial dimensions as a prototype model with symmetry breaking order. The approach is based on computing ground-state wave functions using a tensor network algorithm utilizing a representation with infinite projected entangled-pair states. The results can be applied to any systems with symmetry breaking order, because in the conventional Landau-Ginzburg-Wilson paradigm a quantum system subjected to a phase transition exhibits spontaneous symmetry breaking quantified by a local order parameter. A reduced fidelity bifurcation between two different reduced density matrices is also explored.

Application of shaken lattice interferometry based sensors to space navigation

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory’s (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL’s MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers.

Verification of the mode fidelity and Fried parameter for optical turbulence generated by a spatial light modulator

The ability of a holographic optical spatial mode generator to reproduce a laser beam which has propagated through simulated atmospheric turbulence is characterized via wavefront sensor (WFS) measurements. The range of optical turbulence which can be effectively recreated is determined including its dependence on the pixel resolution of the spatial light modulator used as a hologram to modulate the beam. Optimal sampling of the hologram grating for producing turbulent spatial modes is studied analytically and experimentally. Mode fidelity above 90% is verified up to D/r0 = 10 with less than 3% error in the ratio D/r0. The Fried parameter is verified up to D/r0 = 50 demonstrating less than 9% error producing the targeted ratio D/r0.

Fractional total variation denoising model with [formula omitted] fidelity

We study a nonlocal version of the total variation-based model with L1 fidelity for image denoising, where the regularizing term is replaced with the fractional s-total variation. We discuss regularity of the level sets and uniqueness of solutions, both for high and low values of the fidelity parameter. We analyse in detail the case of binary data given by the characteristic functions of convex sets.

A Novel Approach of ECG Signal Enhancement Using Adaptive Filter Based on Whale Optimization Algorithm

An Electro cardiogram is commonly used in biomedical signal processing. It is used to monitor minor electrical changes in the human body. The electrical changes originate due to the function of heart. The anomalies of heart are found by ECG. In this work the Whale optimization algorithm is used to de-noising the ECG signal. The Whale optimization algorithm is used with Adaptive filter which filter the corrupted ECG signal. The performance of the ANC will be improved by calculating the optimum weight value. The WOA technique gives the best result on the different fidelity parameter compare to PSO, MPSO and ABC. The WOA technique gives the significant improvement in accuracy. It gives a good SNR, MSE, ME result compare to PSO, MPSO and ABC. The WOA gives 80% improvement in SNR 88% in MSE and 89% in ME as compared to PSO. So, by using WOA we get a desired ECG component. The WOA reduces the noise in ECG signal and improves the quality of signal.

A Functional Human-on-a-Chip Autoimmune Disease Model of Myasthenia Gravis for Development of Therapeutics.

Myasthenia gravis (MG) is a chronic and progressive neuromuscular disease where autoantibodies target essential proteins such as the nicotinic acetylcholine receptor (nAChR) at the neuromuscular junction (NMJ) causing muscle fatigue and weakness. Autoantibodies directed against nAChRs are proposed to work by three main pathological mechanisms of receptor disruption: blocking, receptor internalization, and downregulation. Current in vivo models using experimental autoimmune animal models fail to recapitulate the disease pathology and are limited in clinical translatability due to disproportionate disease severity and high animal death rates. The development of a highly sensitive antibody assay that mimics human disease pathology is desirable for clinical advancement and therapeutic development. To address this lack of relevant models, an NMJ platform derived from human iPSC differentiated motoneurons and primary skeletal muscle was used to investigate the ability of an anti-nAChR antibody to induce clinically relevant MG pathology in the serum-free, spatially organized, functionally mature NMJ platform. Treatment of the NMJ model with the anti-nAChR antibody revealed decreasing NMJ stability as measured by the number of NMJs before and after the synchrony stimulation protocol. This decrease in NMJ stability was dose-dependent over a concentration range of 0.01–20 μg/mL. Immunocytochemical (ICC) analysis was used to distinguish between pathological mechanisms of antibody-mediated receptor disruption including blocking, receptor internalization and downregulation. Antibody treatment also activated the complement cascade as indicated by complement protein 3 deposition near the nAChRs. Additionally, complement cascade activation significantly altered other readouts of NMJ function including the NMJ fidelity parameter as measured by the number of muscle contractions missed in response to increasing motoneuron stimulation frequencies. This synchrony readout mimics the clinical phenotype of neurological blocking that results in failure of muscle contractions despite motoneuron stimulations. Taken together, these data indicate the establishment of a relevant disease model of MG that mimics reduction of functional nAChRs at the NMJ, decreased NMJ stability, complement activation and blocking of neuromuscular transmission. This system is the first functional human in vitro model of MG to be used to simulate three potential disease mechanisms as well as to establish a preclinical platform for evaluation of disease modifying treatments (etiology).

Multifidelity modeling for Physics-Informed Neural Networks (PINNs)

Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for this approach are simulation methodologies for which there are fidelity differences connected with significant computational cost differences. Physics-informed Neural Networks (PINNs) are candidates for these types of approaches due to the significant difference in training times required when different fidelities (expressed in terms of architecture width and depth as well as optimization criteria) are employed. In this paper, we propose a particular multifidelity approach applied to PINNs that exploits low-rank structure. We demonstrate that width, depth, and optimization criteria can be used as parameters related to model fidelity and show numerical justification of cost differences in training due to fidelity parameter choices. We test our multifidelity scheme on various canonical forward PDE models that have been presented in the emerging PINNs literature.

Impedance Matching and the Choice Between Alternative Pathways for the Origin of Genetic Coding.

We recently observed that errors in gene replication and translation could be seen qualitatively to behave analogously to the impedances in acoustical and electronic energy transducing systems. We develop here quantitative relationships necessary to confirm that analogy and to place it into the context of the minimization of dissipative losses of both chemical free energy and information. The formal developments include expressions for the information transferred from a template to a new polymer, Iσ; an impedance parameter, Z; and an effective alphabet size, neff; all of which have non-linear dependences on the fidelity parameter, q, and the alphabet size, n. Surfaces of these functions over the {n,q} plane reveal key new insights into the origin of coding. Our conclusion is that the emergence and evolutionary refinement of information transfer in biology follow principles previously identified to govern physical energy flows, strengthening analogies (i) between chemical self-organization and biological natural selection, and (ii) between the course of evolutionary trajectories and the most probable pathways for time-dependent transitions in physics. Matching the informational impedance of translation to the four-letter alphabet of genes uncovers a pivotal role for the redundancy of triplet codons in preserving as much intrinsic genetic information as possible, especially in early stages when the coding alphabet size was small.

A compressed sensing approach to immobilized nanoparticle localization for superparamagnetic relaxometry

Superparamagnetic relaxometry (SPMR) exploits the unique magnetic properties of targeted superparamagnetic iron oxide nanoparticles (SPIOs) to detect small numbers of cancer cells. Reconstruction of the spatial distribution of cancer-bound nanoparticles requires solving an ill-posed inverse problem. The current method, multiple source analysis (MSA), uses a least-squares fit to determine the strength and location of a pre-determined number of magnetic dipoles. In this proof-of-concept study, we propose the application of a sparsity averaged reweighting algorithm (SARA) for volumetric reconstruction of immobilized nanoparticle distributions. We first calibrate the parameters that define the location of the sensors in the forward model of measurement physics. Using this optimized model, we evaluated the performance of the algorithms on various configurations of single and multiple point-source phantoms. We investigated the effect of the data fidelity parameter, voxel size, and iterative reweighting on the reconstruction produced by SARA. We found that the calibrated physics model can predict the detected field values within 5% of the measured data. When only a single source was present, both algorithms were able to detect as little as 0.5 µg of immobilized particles. However, when two sources were measured simultaneously, MSA failed to detect sources containing as much as 10 µg of particles, while SARA detected all of the sources containing at least 5 µg of particles. We show that a suitable data fidelity parameter can be selected objectively, and the total magnitude and location of a point source reconstructed by SARA is not sensitive to voxel size. Detection and localization of multiple small clusters of nanoparticles is a crucial step in SPMR-based diagnostic applications. Our algorithm overcomes the need to know the number of dipoles before reconstruction and improves the sensitivity of the reconstruction when multiple sources are present.

Switching Divergences for Spectral Learning in Blind Speech Dereverberation

When recorded in an enclosed room, a sound signal will most certainly get affected by reverberation. This not only undermines audio quality, but also poses a problem for many human-machine interaction technologies that use speech as their input. In this paper, a new blind, two-stage dereverberation approach based in a generalized $\beta$-divergence as a fidelity term over a non-negative representation is proposed. The first stage consists of learning the spectral structure of the signal solely from the observed spectrogram, while the second stage is devoted to model reverberation. Both steps are taken by minimizing a cost function in which the aim is put either in constructing a dictionary or a good representation by changing the divergence involved. In addition, an approach for finding an optimal fidelity parameter for dictionary learning is proposed. An algorithm for implementing the proposed method is described and tested against state-of-the-art methods. Results show improvements for both artificial reverberation and real recordings.

Continuous-Variable Arbitrated Quantum Signature Based on Dense Coding and Teleportation

Motivated by the dense coding and teleportation of two-mode squeezed vacuum (TMSV) state, a continuous-variable arbitrated quantum signature (CV-AQS) scheme is proposed. The signer Alice generates the signature by the dense coding and sends it to the verifier Bob. The measurement results of two quadrature components serve for the recovery of the secret message. With the help of the arbitrator Charlie, Bob verifies the validity of the signature. The security of the presented protocol is protected by the calculation of the fidelity parameter and the implementation process of two-mode squeezed vacuum (TMSV) state teleportation. Moreover, the proposed scheme can be correctly implemented in the lossy and lossless transmission with a higher capacity.

Fast predictive multi-fidelity prediction with models of quantized fidelity levels

In this paper, we introduce a novel approach for the construction of multi-fidelity surrogate models with “discrete” fidelity levels. The notion of a discrete level of fidelity is in contrast to a mathematical model, for which the notion of refinement towards a high-fidelity model is relevant to sending a discretization parameter toward zero in a continuous way. Our notion of discrete fidelity levels encompasses cases for which there is no notion of convergence in terms of a fidelity parameter that can be sent to zero or infinity. The particular choice of how levels of fidelity are defined in this framework paves the way for using models that may have no apparent physical or mathematical relationship to the target high-fidelity model. However, our approach requires that models can produce results with a common set of parameters in the target model. Hence, fidelity level in this work is not directly representative of the degree of similarity of a low-fidelity model to a target high-fidelity model. In particular, we show that our approach is applicable to competitive ecological systems with different numbers of species, discrete-state Markov chains with a different number of states, polymer networks with a different number of connections, and nano-particle plasmonic arrays with a different number of scatterers. The results of this study demonstrate that our procedure boasts computational efficiency and accuracy for a wide variety of models and engineering systems.

A New Method for Designing of Stable Digital IIR Filter Using Hybrid Method

In this paper, a new technique for designing of a stable digital infinite impulse response filter, with improved performance in passband and stopband regions using quantum particle swarm optimization (QPSO) and artificial bee colony (ABC) algorithm, is explored in frequency domain. In the proposed method, QPSO technique is modified with exploiting the novelty of search and replacement mechanism of scout bee from ABC algorithm. For this purpose, a new design problem is constructed as a nonlinear minimization of mean square error between the designed and desired filter responses in passband, stopband, and transition band simultaneously, allowing permissible ripples in passband and stopband. Efficiency of the proposed technique is measured by several attributes like passband error, stopband error, total squared error (SE), maximum stopband attenuation, maximum passband error $$ \left( {e_{pb}^{\hbox{max} } } \right) $$ , passband ripple, and maximum phase deviation in passband. Experimental results evidence that a significant reduction is achieved in sum of the SE in passband and stopband from 12 to 54%, and the performance is not degraded due to quantization and truncation process. However, computation time in term of CPU time is increased from 0.77 to 4.1%, along with 2.62–7.43% hike in number of function evaluation, when compared to QPSO. A comparative study reveals that the proposed method yields better fidelity parameter as compared other evolutionary algorithms such as gravitational search algorithm, genetic algorithm, and variants of PSO. The proposed technique is also suitable for higher filter taps.

On the local and global minimizers of gradient regularized model with box constraints for image restoration

Recently, nonconvex and nonsmooth models such as those using ‘norm’ have drawn much attention in the area of image restoration. This work investigates the local and global minimizers of the gradient regularized model with box constraints. There are four major ingredients. Firstly, we show that the set of local minimizers can be represented by solutions to some quadratic problems, which are independent of the fidelity parameter α. Based on this, every point satisfying the first-order necessary condition is a local minimizer. Secondly, any two local minimizers have different energy values under certain assumptions, implying the uniqueness of the global minimizer. Thirdly, there exists a uniform lower bound for nonzero gradients of the restored images. Finally, we show that the global minimizer set is piecewise constant in terms of α, and when A is of full column rank and α is large enough, the distance between the true image and the restored images is bounded by the noise level. The numerical examples perfectly demonstrate our theoretical analysis.

A PDE Based Image Segmentation Using Fourier Spectral Method

A PDE based binary image segmentation model using a modified Cahn–Hilliard equation with weaker fidelity parameter ( $$\lambda $$ ) and double well potential has been introduced. The threshold of separation $$\gamma $$ is flexibly chosen between 0 and 1. Convexity splitting is used for time discretization and then Fourier-spectral method is used to solve the proposed modified Cahn–Hilliard equation. The proposed model is tested on bio-medical images.

Technique for QRS complex detection using particle swarm optimisation

A new technique for QRS complex detection of electrocardiogram signals, using particle swarm optimisation (PSO)-based adaptive filter (AF), is proposed. In the proposed method, the AF, based on PSO, is used to generate the feature. An effective detection algorithm, containing search-backs for missed peaks, is also proposed. In the experiment, five PSO variants are tested on MIT-BIH arrhythmia database. The linear decreasing inertia variant of PSO, achieves the best results with sensitivity, positive predictivity and detection error rate of 99.75, 99.83 and 0.42%, respectively. Effectiveness of the proposed method is validated by comparing fidelity parameter of proposed method with state-of-the-art methods.