Classical Equivalents Research Articles

Bi-planar magnetic stabilisation coils for an inertial sensor based on atom interferometry

Inertial sensors that measure the acceleration of ultracold atoms promise unrivalled accuracy compared to classical equivalents. However, atomic systems are sensitive to various perturbations, including magnetic fields, which can introduce measurement inaccuracies. To address this challenge, we have designed, manufactured, and validated a magnetic field stabilisation system for a quantum sensor based on atom interferometry. We solve for the magnetic field generated by surface currents in-between a pair of bi-rectangular coils and approximate the surface current using discrete wires. The wires are wound by-hand onto machined panels which are retrofitted onto the existing mounting structure of the sensor without interfering with any experimental components. Along the central 60mm of the y-axis, which aligns with the trajectory of the atoms during interferometry, the coils are measured to generate an independent uniform axial magnetic field with a strength of Bz=22.81±0.01μT/A [mean±2σstd. error] and an independent linear axial field gradient of strength dBz/dy=10.6±0.1μT/Am. The uniform Bz field is measured to deviate by a maximum value of 1.3% in the same region, which is a factor of three times more uniform than the previously-used on-sensor rectangular Bz compensation set.

Hyperbolic enhancement of a quantum battery

A quantum system which can store energy, and from which one can extract useful work, is known as a quantum battery. Such a device raises interesting issues surrounding how quantum physics can provide certain advantages in the charging, energy storage, or discharging of the quantum battery as compared to their classical equivalents. However, the pernicious effect of dissipation degrades the performance of any realistic battery. Here, we show how one can circumvent this problem of energy loss by proposing a quantum battery model which benefits from quantum squeezing. Namely, charging the battery quadratically with a short temporal pulse induces a hyperbolic enhancement in the stored energy, such that the dissipation present becomes essentially negligible in comparison. Furthermore, we show that when the driving is strong enough, the useful work which can be extracted from the quantum battery, that is the ergotropy, is exactly equal to the stored energy. These impressive properties imply a highly efficient quantum energetic device with abundant amounts of ergotropy. Our theoretical results suggest a possible route to realizing high-performance quantum batteries, which could be realized in a variety of platforms exploiting quantum continuous variables. Published by the American Physical Society 2024

Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm

Quantum computing is an emerging field that can effectively solve several machine learning problems. The immensely growing data size has begun to create barriers for classical machine learning algorithms. Quantum computers proficiently handle and process big data composed of metrics and vectors. This paper presents the quantum machine learning model based on quantum support vector machines (QSVM) to classify brain tumours into malignant and benign. The dataset used in this experiment is Brats 2015, available on Kaggle. The QSVM-based model was run on real-time quantum machines and simulators to extract the performance metrics in terms of accuracy and execution time. The QSVM classification model is compared with its classical SVM equivalent. The kernel-based QSVM model implementation on a 32-qubit quantum simulator was 188 times faster with 95% accuracy which is 1.60% better than the classical equivalent. Similarly, the model takes 24.19% less time than the classical SVM model when realized on a 5-qubit real-time superconducting processor with the same accuracy. Hence, the results reveal that quantum machine learning models implemented on quantum computers outperformed their classical equivalents.

Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage

Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Role of organometallic complexes in olefin polymerization: a review report

• A detailed discussion on various organometallic complexes used for olefin polymerization, has been done. • Regulation on composition and microstructure of the polymer products can be done by altering the metal center and the auxiliary ligand of an organometallic complex. • The organometallic catalysts of group 3 metals for the polymerization of olefins are still less discovered and very uncommon and group 4 metal-based catalysts holding diverse auxiliary ligands have been widely researched and investigated. • Increase in performance of homogeneous catalysts by introducing them on metalasiloxanes. The chemistry of catalytic polymerization of ethylene and propylene employing titanium complexes and aluminum-alkyls as co-catalysts is well known since last 50 years. Thorough studies show that invention of homogeneous organometallic complexes as catalysts such as metallocenes and other transition metal complexes with methylaluminoxane (MAO) as co-catalyst for the polymerization of olefins has achieved a lot of interest in this area as compared to the well-known and conventional heterogeneous catalysts i.e. Ziegler–Natta and Phillips catalyst. The possibilities of controlling the polymer composition, microstructure, tacticity, stereoregularity and special properties with high precision are found to be high in homogeneous catalysts. Availability of single active accessible site on metallocene catalysts in contrast to their classical equivalents is responsible for improved catalytic polymerization. Furthermore, these single-site catalysts, such as half titanocenes/MAO, zirconocenes, and other single-site catalysts, can control important parameters like co-monomer distribution, molecular weight, molecular weight distribution, molecular architecture, stereo-specificity, degree of linearity, polymer branching etc . The purity in products found less when Ziegler–Natta catalysts were used. This review summarises and compares the recent advances in the polymerization of various olefins (especially ethylene or α-olefins) using various organometallic catalysts consist of rare-earth elements and lanthanides, group 4 metals, and other transition metals (V,Cr and Ni). At the end the review discusses modulation of catalytic activity to control polymerization reactions and concludes the study with future challenges in the area.

Bibliometric Survey of Quantum Machine Learning

ABSTRACT Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these being published in the last four years (2016–2019). This essentially translates to one patent for every two publications. The Web of Science database, though bringing out 154 publications in the period, shows similar trends across the metrics. We have carried out a comparative study of some of the metrics in Scopus and Web of Science databases. Overall the study identifies the top 10 Institutions, authors, and research journals.

Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare

Mobile healthcare is a promising approach. It is realized by using the biomedical implants that are connected to the cloud. A framework for the precise and effective diagnosis of epileptic seizures is designed in this context. To achieve real-time compression and effective signal processing and transmission, it uses an intelligent event-driven electroencephalogram (EEG) signal acquisition. Experimental results show that grace of the event-driven nature an overall 3.3 fold compression and transmission bandwidth usage reduction is achieved by the devised method compared to the conventional counterparts. It promises a notable decrease in the post analysis and classification processing activity. The system performance is studied by using a standard three class EEG epileptic seizure dataset. The highest classification accuracy of 97.5% is secured for a mono-class. The best average classification accuracy of 96.4% is attained for three-classes. Comparison of the system with classical equivalents is made. Results demonstrate more than threefold and sevenfold of outperformance respectively in terms of compression gain and processing efficiency while confirming a comparable classification precision.

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

The Coupled Cluster (CC) method is used to compute the electronic correlation energy in atoms and molecules and often leads to highly accurate results. However, due to its single-reference nature, standard CC in its projected form fails to describe quantum states characterized by strong electronic correlations and multi-reference projective methods become necessary. On the other hand, quantum algorithms for the solution of many-electron problems have also emerged recently. The quantum unitary variant of CC (UCC) with singles and doubles (q-UCCSD) is a popular wavefunction Ansatz for the variational quantum eigensolver algorithm. The variational nature of this approach can lead to significant advantages compared to its classical equivalent in the projected form, in particular, for the description of strong electronic correlation. However, due to the large number of gate operations required in q-UCCSD, approximations need to be introduced in order to make this approach implementable in a state-of-the-art quantum computer. In this work, we evaluate several variants of the standard q-UCCSD Ansatz in which only a subset of excitations is included. In particular, we investigate the singlet and pair q-UCCD approaches combined with orbital optimization. We show that these approaches can capture the dissociation/distortion profiles of challenging systems, such as H4, H2O, and N2 molecules, as well as the one-dimensional periodic Fermi-Hubbard chain. These results promote the future use of q-UCC methods for the solution of challenging electronic structure problems in quantum chemistry.

Simulation of quantum walks and fast mixing with classical processes

We compare discrete-time quantum walks on graphs to their natural classical equivalents, which we argue are lifted Markov chains (LMCs), that is, classical Markov chains with added memory. We show that LMCs can simulate the mixing behavior of any quantum walk, under a commonly satisfied invariance condition. This allows us to answer an open question on how the graph topology ultimately bounds a quantum walk's mixing performance, and that of any stochastic local evolution. The results highlight that speedups in mixing and transport phenomena are not necessarily diagnostic of quantum effects, although superdiffusive spreading is more prominent with quantum walks. The general simulating LMC construction may lead to large memory, yet we show that for the main graphs under study (i.e., lattices) this memory can be brought down to the same size employed in the quantum walks proposed in the literature.

Provably safe robot navigation with obstacle uncertainty

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most collision probability (-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.

Classical Lagrangians and Finsler structures for the nonminimal fermion sector of the standard model extension

This article is devoted to finding classical point-particle equivalents for the fermion sector of the nonminimal standard model extension (SME). For a series of nonminimal operators, such Lagrangians are derived at first order in Lorentz violation using the algebraic concept of Gr\"obner bases. Subsequently, the Lagrangians serve as a basis for reanalyzing the results of certain kinematic tests of special relativity that were carried out in the past century. Thereby, a number of new constraints on coefficients of the nonminimal SME is obtained. In the last part of the paper we point out connections to Finsler geometry.

Pagan Virtue in a Christian World: Sigismondo Malatesta and the Italian Renaissance by Anthony F. D’Elia

Reviewed by: Pagan Virtue in a Christian World: Sigismondo Malatesta and the Italian Renaissance by Anthony F. D’Elia Costanza Gislon Dopfel Pagan Virtue in a Christian World: Sigismondo Malatesta and the Italian Renaissance. By Anthony F. D’Elia. (Cambridge, MA: Harvard University Press. 2016. Pp. xii, 355. $39.95. ISBN 978-0-674-08851-1.) A boon for period scholars and anyone interested in Malatesta history, Pagan Virtue in a Christian World: Sigismondo Malatesta and the Italian Renaissance offers a structured approach to the works of the humanists at the court of Sigismondo Malatesta as well as a comprehensive and impartial representation of a crucial intellectual period. Through Anthony D’Elia’s extensive research, friends, enemies, poets, and artists appear on the court stage—not simply in terms of their functions in relation to Malatesta but on their own terms, as individual scholars. Sigismondo surfaces as the unabashed champion of a resurrected classicism imbued with neo-Platonic philosophy in the burgeoning style of other contemporary intellectual circles, including that of Cosimo de’ Medici. The result was a surprisingly sophisticated environment that pushed the boundaries of philosophy and theology ahead of its time, offering an outstanding target for Pope Pius II’s indignation with an intellectual trend that possibly threatened the Christian faith. Condemned to eternal damnation by Pius in what D’Elia calls a “reverse canonization” (p. 1), Sigismondo’s downfall was made an example for a whole world to behold. The title is indicative of the underlying question: was Sigismondo truly a pagan, or was he a devout Christian whose faith was simply cloaked in classical equivalents? D’Elia integrates an analysis of the notorious artwork of the Tempio Malatestiano with a thorough inquiry of the lesser known court literature, which sheds new light on the purpose of both. With comparisons to Odysseus and Achilles, Sigismondo assumes a superhuman character in the works of his poets Porcellio and Basinio, whereas Valturio, the chronicler Broglio, and even the friar de’ Cocchi depict a man immersed in pagan thought and religion, who loved to discuss platonic philosophy and the nature of the soul. Famous for his daring courage, handsome looks, and intellectual curiosity, the real Sigismondo was indeed a larger-than-life character, who could inspire both terror and worship. The move from man to god, or at least to extraordinary fiend, was therefore not out of reach, and Pius’s reaction can be understood, if not explained. As D’Elia concedes, Pius II was right in the sense that “much of the literature at Sigismondo’s court was so contaminated with paganism and implicated in his military exploits that it could be deemed heretical” (p. 183). The authority allowed by a pagan morality, which validates all actions for the sake of immortal fame, justifies Sigismondo in ways impossible within a Christian [End Page 604] viewpoint. In this context, the violent heroes of the battlefield and the reckless pursuers of knowledge are equally destined for the Elysian Fields and Apollo’s wreath—and Sigismondo doubly so, as he represents both virtues. Indeed, behind the pagan image cultivated by court poets lurks a progressive political idea: Sigismondo is the ante litteram figure of the Prince: terrible, grand, and godlike in his divine task to save Italy from foreign invaders. As D’Elia explains, “Basinio’s Hesperis is outlandishly nationalistic. Its very name, Hesperis, is an ancient word for Italy” (p. 117). Paganism also justifies the relationship between Sigismondo and the young Isotta, whose beauty was celebrated in medals and poems even while Sigismondo’s second wife was still living. The poems by Basinio and Porcellio elevate the girl to goddess through the device of an imaginary death, which should, but does not, lessen the sexually charged character of the relationship. Her very tomb, D’Elia notes, was inscribed with the date of the consummation of their affair. Rooted in the foundation of humanistic paideia, Sigismondo emerges as the mind behind the cultural plan that brought together a revival of classical art, poetry, and philosophy. But although the contamination of Christian ideas allowed a pagan culture to flourish, the agents of such transformation did not necessarily view themselves as pagans. And yet...

Eikonal approximation, Finsler structures, and implications for Lorentz-violating photons in weak gravitational fields

The current article shall contribute to understanding the classical analogue of the minimal photon sector in the Lorentz-violating Standard-Model Extension (SME). It is supposed to complement all studies performed on classical point-particle equivalents of SME fermions. The classical analogue of a photon is not a massive particle being described by a usual equation of motion, but a geometric ray underlying the eikonal equation. The first part of the paper will set up the necessary tools to understand this correspondence for interesting cases of the minimal SME photon sector. In conventional optics the eikonal equation follows from an action principle, which is demonstrated to work in most (but not all) Lorentz-violating cases as well. The integrands of the action functional correspond to Finsler structures, which establishes the connection to Finsler geometry. The second part of the article treats Lorentz-violating light rays in a weak gravitational background by implementing the principle of minimal coupling. Thereby it is shown how Lorentz violation in the photon sector can be constrained by measurements of light bending at massive bodies such as the Sun. The phenomenological studies are based on the currently running ESA mission GAIA and the planned NASA/ESA mission LATOR. The final part of the paper discusses certain aspects of explicit Lorentz violation in gravity based on the setting of Finsler geometry.

Intricacies of quantum computational paths

Graph search represents a cornerstone in computer science and is employed when the best algorithmic solution to a problem consists in performing an analysis of a search space representing computational possibilities. Typically, in such problems it is crucial to determine the sequence of transitions performed that led to certain states. In this work we discuss how to adapt generic quantum search procedures, namely quantum random walks and Grover's algorithm, in order to obtain computational paths. We then compare these approaches in the context of tree graphs. In addition we demonstrate that in a best-case scenario both approaches differ, performance-wise, by a constant factor speedup of two, whilst still providing a quadratic speedup relatively to their classical equivalents. We discuss the different scenarios that are better suited for each approach.

MATLAB library LIBRA

AbstractLIBRA stands for ‘library for robust analysis.’ It is a MATLAB toolbox mainly containing implementations of robust statistical methods. Robust statistics is involved with the detection of aberrant observations, also called outliers. The aim of robust statistical methods is to provide estimates which are not affected by the nonregular observations and which are then able to pinpoint the outliers. The robust methods implemented in LIBRA nowadays cover: univariate location, scale (Qn) and skewness estimation (Medcouple), covariance estimation (FAST MCD), regression (FAST‐LTS, MCD regression, depth quantiles), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares regression (RSIMPLS), classification (RDA, RSIMCA) and several methods to deal with skewed data. Besides that, the toolbox contains various methods for cluster analysis, many graphical diagnostic tools, and classical equivalents of several implemented methods. Only a few of the recently added methods will be highlighted in this paper. The features of the LIBRA functions will be illustrated by means of some real data sets. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Software for Computational Statistics > Software/Statistical Software

Transient Power System Analysis With Measurement-Based Gray Box and Hybrid Dynamic Equivalents

The paper addresses practical capabilities of artificial neural networks (ANNs) in developing measurement-based continuous-time dynamic equivalents for power systems. Our method is based on a set of measurements at boundary nodes between a subsystem that is to be modeled in detail ("retained" portion of the system) and the part that is to be replaced by a simplified ("equivalent") model. We are particularly interested in combining standard physics-based models with signal-based models derived from measurements. We utilize a color-coding scheme to distinguish between physics-based models (clear or white box) at one end, the signal-based models (opaque or black box) at the opposite end, and mixed (gray box) models in the middle. The paper also proposes a way for combining classical and ANN-based equivalents in a hybrid model implemented in a standard software environment for transient analysis (in this case, ETMSP). Our conclusions are based on simulations performed on a model of a benchmark multimachine power system derived from the WSCC system.

Competition of random and periodic potentials in interacting fermionic systems and classical equivalents: The Mott glass

We study the competition between a random potential and a commensurate potential on interacting fermionic and bosonic systems using a variety of methods. We focus on one-dimensional interacting fermionic systems, but higher-dimensional bosonic and fermionic extensions, as well as classical equivalents, are also discussed. Our methods, which include the bosonization method, the replica variational method, the functional renormalization group method, and perturbation around the atomic limit, go beyond conventional perturbative expansions around the Luttinger liquid in one dimension. All these methods agree on the prediction in these systems of a phase, the Mott glass, intermediate between the Anderson (compressible, with a pseudogap in the optical conductivity) and the Mott (incompressible with a gap in the optical conductivity) insulator. The Mott glass, which was unexpected from a perturbative renormalization-group point of view has a pseudogap in the conductivity while remaining incompressible. Having derived the existence of a Mott glass phase in one dimension, we show qualitatively that its existence can also be expected in higher dimensions. We discuss the relevance of this phase to experimental systems such as disordered classical elastic systems and dirty bosons.

Mutlivariate calibration with Raman data using fast principal component regression and partial least squares methods

Linear and non-linear calibration methods (principal component regression (PCR), partial least squares regression (PLS), and neural networks (NN)) were applied to a slightly non-linear Raman data set. Because of the large size of this data set, recently introduced linear calibration methods, specifically optimised for speed, were also used. These fast methods achieve speed improvement by using the Lanczos decomposition for the singular value decomposition steps of the calibration procedures, and for some of their variants, by optimising the models without cross-validation (CV). Linear methods could deal with the slight non-linearity present in the data by including extra components, therefore, performing comparably to NNs. The fast methods performed as well as their classical equivalents in terms of precision in prediction, but the results were obtained considerably faster. It, however, appeared that CV remains the most appropriate method for model complexity estimation.

A loop antenna in front of a resistive sheet

Formulas for self inductance, mutual inductance, and image for a horizontal loop placed above a resistive sheet are presented. These results extend the magnetostatically-derived self- and mutual-inductance concepts to account for the finite speed of propagation of the fields. Classical network equivalents are introduced for low- and high-resistive sheet values. For capacitive sheets, the structure is capable of selective radiation (eminently below the plane of the sheet).

Renormalization-group study of the Hamiltonian version of the Potts model. III. Improved results for larger cells

The renormalization-group transformation, applied earlier to the Hamiltonian Potts model to study the crossover from second-order to first-order transition as the number of states $q$ increases, is extended by taking larger cells in the block transformation. The results improve systematically with increasing block size. The critical value of $q$ above which the transition is of first order, seems to converge to 5 instead of the exactly known value ${q}_{c}=4$. The classical equivalents of the new couplings, which drive the system to the first-order transition, are discussed.