Classical Quantum Research Articles

A novel nonlinear prestack inversion method based on nutcracker optimization algorithm with high convergence speed

Predicting reservoir parameters with high accuracy is still a crucial work of oil reservoir exploration and development. Due to the limitation of computational efficiency, deterministic methods are primarily used in practical production applications for predicting reservoir parameters. When the nonlinear forward equations are exceptionally complex and the initial model constructed deviates significantly from the true reservoir parameters, deterministic methods may have difficulty obtaining reasonable predictions of reservoir parameters. Compared to deterministic methods, intelligent optimization methods based on nature-inspired metaheuristic algorithms have unique advantages because they do not require derivative information, can achieve global optimization, and have less reliance on initial model. Therefore, they perform better in solving complex nonlinear optimization problems. In this paper, a new intelligent optimization algorithm called Nutcracker Optimization Algorithm (NOA) with a high convergence speed is introduced. By utilizing this optimization algorithm to solve the nonlinear inversion problem constructed by the highly nonlinear exact Zoeppritz equations, we analyze the potential of nonlinear reservoir parameters prediction methods based on intelligent optimization algorithms in practical production applications. The synthetic data test shows that, compared to the classical quantum particle swarm optimization (QPSO) algorithm and the highly-cited whale optimization algorithm (WOA), the prestack nonlinear inversion method based on NOA proposed in this paper ensures high convergence accuracy and exhibits high computational efficiency. It significantly reduces computation time and holds great potential for practical production applications. The field data test shows that the proposed method can rapidly and accurately estimates reservoir parameters, validating the feasibility and effectiveness of the proposed method. This has important theoretical value and practical significance for advancing the application of intelligent optimization algorithms in field reservoir exploration and development.

Gravitational bremsstrahlung from Yukawa and nucleon collisions

We obtain the gravitational emission from particles scattering via the Yukawa interaction, presenting both classical and approximate quantum results. We also estimate the contribution from the tensor part of the internucleon interaction. This emission is the main source of a very high frequency component to the stochastic background in the Solar System and in neutron stars. The emission from the Sun (allowing for Debye screening) and from a typical neutron star are obtained. The gravitational wave luminosity of the Sun is 41±10 MW. Published by the American Physical Society 2024

The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

Entropy-based geometric measures of correlations for bell diagonal states via measurement

Abstract The classification and quantification of quantum correlations are the important issues in quantum information theory. Quantum relative entropy and Rényi relative entropy are both non-negative, and can therefore be regarded as generalized distances between quantum states. We first introduced the method proposed by Brodutch and Modi for constructing measurements of classical and quantum correlations. Subsequently, we use this method to construct measurement-induced geometric classical and quantum correlations based on quantum relative entropy and Rényi− 1 2 relative entropy. For two-qubit Bell diagonal states, the analytic expressions for these geometric classical correlations and quantum correlations are obtained.

Quantum Exclusion Process driven by Brownian Motions in terms of quantum Bernoulli noises

Quantum Bernoulli noises are the family of annihilation and creation operators on Bernoulli functionals, which satisfy a canonical anti-commutation relation in equal time. The paper is devoted to the study of Quantum Exclusion Process driven by Brownian Motions in terms of Quantum Bernoulli Noises. We first study the existence and uniqueness of regular solutions to the classical Quantum Exclusion Process driven by Brownian Motions. We then provide an explicit representation formula by separating the action on off-diagonal and diagonal operators, on which they are reduced to the Quantum Exclusion Process of classical Markov chains. Finally, we prove that the existence of regular invariant measures for Quantum Exclusion Process driven by Brownian Motions in terms of Quantum Bernoulli Noises.

Quantum synchronization between two spin chains using pseudo-bosonic equivalence

Quantum synchronization among many spins is an intriguing domain of research. In this paper, we explore the quantum synchronization of two finite chains of spin-1/2 particles, via a nonlinear interaction mediated by a central intermediary spin chain. We introduce a novel approach using the Holstein-Primakoff transformation to treat the spin chains as pseudo-bosonic systems and thereby applying the synchronization criteria for harmonic oscillators. Our theoretical framework and numerical simulations reveal that under optimal conditions, the spin chains can achieve both classical and perfect quantum synchronization. We show that quantum synchronization is robust against variations in the number of spins and inter-spin coupling, though may be affected by thermal noise. This work advances the understanding of synchronization in multi-spin systems and introduces a generalized synchronization measure for both bosons and fermions.

RadicalPy: A Tool for Spin Dynamics Simulations.

Radical pairs (electron-hole pairs, polaron pairs) are transient reaction intermediates that are found and exploited in all areas of science, from the hard realm of physics in the form of organic semiconductors, spintronics, quantum computing, and solar cells to the soft domain of chemistry and biology under the guise of chemical reactions in solution, biomimetic systems, and quantum biology. Quantitative analysis of radical pair phenomena has historically been successful by a few select groups. With this in mind, we present an intuitive open-source framework in the Python programming language that provides classical, semiclassical, and quantum simulation methodologies. A radical pair kinetic rate equation solver, Monte Carlo-based spin dephasing rate estimations, and molecule database functionalities are implemented. We introduce the kine-quantum method, a new approach that amalgamates classical rate equations, semiclassical, and quantum techniques. This method resolves the prohibitively large memory requirement issues of quantum approaches while achieving higher accuracy, and it also offers wavelength-resolved simulations, producing time- and wavelength-resolved magnetic field effect simulations. Model examples illustrate the versatility and ease of use of the software, including the new approach applied to the magnetosensitive absorption and fluorescence of flavin adenine dinucleotide photochemistry, spin-spin interaction estimation from molecular dynamics simulations on radical pairs inside reverse micelles, radical pair anisotropy inside proteins, and triplet exciton pairs in anthracene crystals. The intuitive interface also allows this software to be used as a teaching or learning aid for those interested in the field of spin chemistry. Furthermore, the software aims to be modular and extensible, with the aim to standardize how spin dynamics simulations are performed.

Hybridization of Learning Techniques and Quantum Mechanism for IIoT Security: Applications, Challenges, and Prospects

This article describes our point of view regarding the security capabilities of classical learning algorithms (CLAs) and quantum mechanisms (QM) in the industrial Internet of Things (IIoT) ecosystem. The heterogeneity of the IIoT ecosystem and the inevitability of the security paradigm necessitate a systematic review of the contributions of the research community toward IIoT security (IIoTsec). Thus, we obtained relevant contributions from five digital repositories between the period of 2015 and 2024 inclusively, in line with the established systematic literature review procedure. In the main part, we analyze a variety of security loopholes in the IIoT and categorize them into two categories—architectural design and multifaceted connectivity. Then, we discuss security-deploying technologies, CLAs, blockchain, and QM, owing to their contributions to IIoTsec and the security challenges of the main loopholes. We also describe how quantum-inclined attacks are computationally challenging to CLAs, for which QM is very promising. In addition, we present available IIoT-centric datasets and encourage researchers in the IIoT niche to validate the models using the industrial-featured datasets for better accuracy, prediction, and decision-making. In addition, we show how hybrid quantum-classical learning could leverage optimal IIoTsec when deployed. We conclude with the possible limitations, challenges, and prospects of the deployment.

Many-body localized hidden generative models

Quantum generative models hold the promise of accelerating or improving machine learning tasks by leveraging the probabilistic nature of quantum states, but the successful optimization of these models remains a difficult challenge. To tackle this challenge, we present a new architecture for quantum generative modeling that combines insights from classical machine learning and quantum phases of matter. In particular, our model utilizes both many-body localized (MBL) dynamics and hidden units to improve the optimization of the model. We demonstrate the applicability of our model on a diverse set of classical and quantum tasks, including a toy version of MNIST handwritten digits, quantum data obtained from quantum many-body states, and nonlocal parity data. Our architecture and algorithm provide novel strategies of utilizing quantum many-body systems as learning resources and reveal a powerful connection between disorder, interaction, and learning in quantum many-body systems. Published by the American Physical Society 2024

Dephasing dynamics of a central spin interacting with a classical Ising spin bath

We study the effect of interaction between bath spins on central spin dynamics. For this end, we consider various types of interactions among the bath fluctuators. We give an analytical relation for the dephasing dynamics of a central spin homogeneously interacting with a bath of classically interacting spins in thermal equilibrium. The obtained exact relation for the central spin dynamics depends on the average of the number of bath spins N in the canonical ensemble. We find the relation of dephasing time with bath's temperature and strength of interaction between bath spins. We show that if the classically interacting bath is large enough, off-diagonal coherence will decay into Gaussian form. This Gaussian decay is valid for temperatures higher than critical temperature. Moreover, we analytically prove the equivalence of dephasing dynamics for central spin models with classical Ising spin bath and quantum flip-flop spin bath interactions. Therefore, the dephasing dynamics for central spin models with quantum spin bath interactions can be reproduced with much less expensive classical spin bath interaction model instead. Our results can be applied in a number of quantum systems used in quantum sensing such as color centers in solids or quantum dots manuscript.

Tachyonic production of dark relics: classical lattice vs. quantum 2PI in Hartree truncation

We study the out-of-equilibrium production of non-minimally coupled self-interacting scalar dark matter during reheating using classical lattice simulations. The outcomes of the classical simulations are in qualitative agreement with the previous results obtained using the quantum 2PI approach in the Hartree truncation. In particular, the novel non-linear resonance found in the 2PI Hartee study is present also in the classical lattice simulations and can dominate the final dark matter yield. For the parameters considered, the difference in final value of the scalar two-point function between the two approaches is a factor of O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(1).

ON THE NATURE OF TIME AND ITS VARIOUS PHYSICAL THEORIES PERCEPTION

Time is one of the most important and mysterious concepts for mankind. It plays an important role in the lives of each of us, it is widely used in many scientific fields, especially in physics and philosophy, but there are many questions about its true nature. This article discusses the perception of time in physical theories and its basic concepts, and compares the concepts of time in classical mechanics, relativity theory, and quantum mechanics. Specific characteristics of time in each theory and its influence on physical processes are considered. It also discusses the nature of time, its disappearance, and theoretical disagreements. The study brings together the philosophical and scientific aspects of the concept of time, offering a new perspective for those interested in learning.

Quantum-Enhanced Generalized Pattern Search Optimization

While the development of quantum computers promises a myriad of advantages over their classical counterparts, care must be taken when designing algorithms that substitute a classical technique with a potentially advantageous quantum method. The probabilistic nature of many quantum algorithms may result in new behavior that could negatively impact the performance of the larger algorithm. The purpose of this work is to preserve the advantages of applying quantum search methods for generalized pattern search algorithms (GPSs) without violating the convergence criteria. It is well known that quantum search methods are able to reduce the expected number of oracle calls needed for finding the solution to a search problem from O(N) to O(N) However, the number of oracle calls needed to determine that no solution exists with certainty is exceedingly high and potentially infinite. In the case of GPS, this is a significant problem since overlooking a solution during an iteration will violate a needed assumption for convergence. Here, we overcome this problem by introducing the quantum improved point search (QIPS), a classical–quantum hybrid variant of the quantum search algorithm QSearch. QIPS retains the O(N) oracle query complexity of QSearch when a solution exists. However, it is able to determine when no solution exists, with certainty, using only O(N) oracle calls.

Quantum Information Driven Ansatz (QIDA): Shallow-Depth Empirical Quantum Circuits from Quantum Chemistry.

Hardware-efficient empirical variational ansätze for Variational Quantum Eigensolver (VQE) simulations of quantum chemistry often lack a direct connection to classical quantum chemistry methods. In this work, we propose a method to bridge this gap by introducing a novel approach to constructing a starting point for variational quantum circuits, leveraging quantum mutual information from classical quantum chemistry states to design simple yet effective heuristic ansätze with a topology reflecting the molecular system's correlations. As a first step, we make use of quantum chemistry calculations, such as Mo̷ller-Plesset (MP2) perturbation theory, to initially provide approximate Natural Orbitals, which have been shown to be the best candidate one-electron basis for developing compact empirical wave functions.1 Second, we evaluate the quantum mutual information matrix, which provides insights about the main correlations between qubits of the quantum circuit, and enables a direct design of entangling blocks for the circuit. The resulting ansatz is then used with a VQE to obtain a short-depth variational ground state of the electronic Hamiltonian. To validate our approach, we perform a comprehensive statistical analysis through simulations of various molecular systems (H2, LiH, H2O) and apply it to the more complex NH3 molecule. The reported results demonstrate that the proposed methodology gives rise to highly effective ansätze, outperforming the standard empirical ladder-entangler ansatz. Overall, our approach can be used as an effective state preparation, providing a promising route for designing efficient variational quantum circuits for large molecular systems.

Proposition of Optimization of the Meetinthemiddle Protocol for Quantum Repeaters

Quantum computing relies on concepts derived from classical information theory and quantum physics. In order for quantum communications to be a reality, it is essential to have the most advanced infrastructures in place. In this paper, we explore the significance of quantum communication and the utilization of quantum repeaters, which rely on the phenomenon of quantum entanglement. We present the MeetInTheMiddle protocol for quantum repeaters and its total transmission probability, and then propose an improvement of this protocol based on the comparison of the total transmission probability.

Quantum visual feature encoding revisited

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed the “Quantum Information Gap” (QIG), leads to an information gap between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization for that found and underscore the significance of QIG, as it directly impacts the performance of quantum machine learning algorithms. To tackle this challenge, we introduce a simple but efficient new loss function named Quantum Information Preserving (QIP) to minimize this gap, resulting in enhanced performance of quantum machine learning algorithms. Extensive experiments validate the effectiveness of our approach, showcasing superior performance compared to current methodologies and consistently achieving state-of-the-art results in quantum modeling.

InP/LiNbO3 Covalent Heterointerface Construction via an Asymmetric Plasma Activation Strategy for Hybrid Integrated Quantum Systems.

Lithium niobate (LiNbO3) is emerging as an appealing candidate for integrated optical applications with enhanced complexity, owing to its inherent abundant optoelectronic properties. To compensate for the inability of LiNbO3 to generate indistinguishable single photons, the evanescent coupling heterointerface constructed between III-V compound semiconductors (e.g., InP) and LiNbO3 through plasma activation provides a feasible solution for balancing the integration efficiency and interfacial stability while achieving sub-50 nm alignment accuracy between devices, thus offering ultracompact on-chip light sources for classical optoelectronics and quantum optics. However, a challenge remains in the formation of the InP/LiNbO3 platform due to the huge mismatch in the coefficient of thermal expansion. Here, we demonstrate the InP/LiNbO3 covalent heterointerface using an asymmetric plasma activation strategy. Different plasmas are used for the activation of InP and LiNbO3 specifically, balancing the enhancement of surface functional group density with the avoidance of defect generation effectively. More importantly, combined with surface comprehensive characterizations and interface performance, we determine that the introduction of ammonia solution enables the surface hydroxyl groups to be "effective" as LiNbO3 surface relaxation increases the chance of -OH groups' contact. Therefore, a robust covalent bond network is established across the InP/LiNbO3 interface at 80 °C with an enhanced bonding strength of 9.7 MPa. Moreover, a hybrid quantum photonic chip based on the InP/LiNbO3 platform is designed to compute the coupling efficiency and the impact of misalignment on it, demonstrating the potential of extending the platform to hybrid integrated quantum systems.

Kac–Ward Solution of the 2D Classical and 1D Quantum Ising Models

AbstractWe give a rigorous derivation of the free energy of (i) the classical Ising model on the triangular lattice with translation-invariant coupling constants and (ii) the one-dimensional quantum Ising model. We use the method of Kac and Ward. The novel aspect is that the coupling constants may have negative signs. We describe the logarithmic singularity of the specific heat of the classical model and the validity of the Cimasoni–Duminil-Copin–Li formula for the critical temperature. We also discuss the quantum phase transition of the quantum model.

Molecular modelling of luciferyl adenylate deprotonation in the active site of Photinus pyralis luciferase

Firefly bioluminescence is a product of chemical reactions that involve luciferin chromophore oxidation in the active site of luciferase proteins. We perform a series of classical molecular dynamic simulations and combined quantum and molecular mechanics (QM/MM) calculations to expose the molecular mechanism of C4 carbon atom deprotonation in luciferyl adenylate molecule. QM/MM calculations confirm that ND-protonated His245 residue is a suitable proton acceptor in the wild-type Photinus pyralis luciferase. Classical molecular dynamic simulations reveal oxygen-binding cavities inside the protein including the one located close to the C4 atom of luciferin. In mutant forms that lack direct interactions with the His245 side chain, a proton wire comprising water molecules stimulates either protonation of the phosphate group of the luciferyl adenylate forming an unstable intermediate or keto–enol tautomerisation of luciferin. The comparison of the ionisation potentials of molecular systems with the ‘deprotonated’ C4 carbon atom revealed that the ionisation energy for the enol tautomeric form is close to the system with the His245 proton acceptor. Thus, the existence of the keto–enol tautomerisation channel might explain bioluminescence in case of the absence of the amino acid proton acceptor.

DMDA-PatA mediates RNA sequence-selective translation repression by anchoring eIF4A and DDX3 to GNG motifs

Small-molecule compounds that elicit mRNA-selective translation repression have attracted interest due to their potential for expansion of druggable space. However, only a limited number of examples have been reported to date. Here, we show that desmethyl desamino pateamine A (DMDA-PatA) represses translation in an mRNA-selective manner by clamping eIF4A, a DEAD-box RNA-binding protein, onto GNG motifs. By systematically comparing multiple eIF4A inhibitors by ribosome profiling, we found that DMDA-PatA has unique mRNA selectivity for translation repression. Unbiased Bind-n-Seq reveals that DMDA-PatA-targeted eIF4A exhibits a preference for GNG motifs in an ATP-independent manner. This unusual RNA binding sterically hinders scanning by 40S ribosomes. A combination of classical molecular dynamics simulations and quantum chemical calculations, and the subsequent development of an inactive DMDA-PatA derivative reveals that the positive charge of the tertiary amine on the trienyl arm induces G selectivity. Moreover, we identified that DDX3, another DEAD-box protein, is an alternative DMDA-PatA target with the same effects on eIF4A. Our results provide an example of the sequence-selective anchoring of RNA-binding proteins and the mRNA-selective inhibition of protein synthesis by small-molecule compounds.