Macroscopic State Research Articles

Effects of local decoherence on quantum critical metrology

The diverging responses to parameter variations of systems at quantum critical points motivate schemes of quantum critical metrology (QCM), which feature sub-Heisenberg scaling of the sensitivity with the system size (e.g., the number of particles). This sensitivity enhancement by quantum criticality is rooted in the formation of Schr\"odinger cat states, or macroscopic superposition states at the quantum critical points. The cat states, however, are fragile to decoherence caused by coupling to local environments, since the local decoherence of any particle would cause the collapse of the whole cat state. Therefore, it is unclear whether the sub-Heisenberg scaling of QCM is robust against the local decoherence. Here we study the effects of local decoherence on QCM, using a one-dimensional transverse-field Ising model as a representative example. We find that the standard quantum limit is recovered by single-particle decoherence. Using renormalization group analysis, we demonstrate that the noise effects on QCM is general and applicable to many universality classes of quantum phase transitions whose low-energy excitations are described by a ${\ensuremath{\phi}}^{4}$ effective field theory. Since in general the many-body entanglement of the ground states at critical points, which is the basis of QCM, is fragile to quantum measurement by local environments, we conjecture that the recovery of the standard quantum limit by local decoherence is universal for QCM using phase transitions induced by the formation of long-range order. This work demonstrates the importance of protecting macroscopic quantum coherence for quantum sensing based on critical behaviors.

Macroscopic Superposition States in Isolated Quantum Systems

For any choice of initial state and weak assumptions about the Hamiltonian, large isolated quantum systems undergoing Schrodinger evolution spend most of their time in macroscopic superposition states. The result follows from von Neumann's 1929 Quantum Ergodic Theorem. As a specific example, we consider a box containing a solid ball and some gas molecules. Regardless of the initial state, the system will evolve into a quantum superposition of states with the ball in macroscopically different positions. Thus, despite their seeming fragility, macroscopic superposition states are ubiquitous consequences of quantum evolution. We discuss the connection to many worlds quantum mechanics.

Coherent Superpositions of Photon Creation Operations and Their Application to Multimode States of Light.

We present a concise review of recent experimental results concerning the conditional implementation of coherent superpositions of single-photon additions onto distinct field modes. Such a basic operation is seen to give rise to a wealth of interesting and useful effects, from the generation of a tunable degree of entanglement to the birth of peculiar correlations in the photon numbers and the quadratures of multimode, multiphoton, states of light. The experimental investigation of these properties will have an impact both on fundamental studies concerning, for example, the quantumness and entanglement of macroscopic states, and for possible applications in the realm of quantum-enhanced technologies.

Creep-fatigue interactions in type 316H under typical high-temperature power plant operating conditions

A micromechanical crystal plasticity self-consistent model (SCM) is used to analyse loading histories in 316H stainless steel. SCM predictions on changes in cyclic and creep properties are compared to analyses via the R5 assessment procedure. Plant-relevant cyclic-creep histories at 550 °C are examined with focus on the estimation of accumulated creep strain. The effect of cyclic plasticity on creep is quantified. The levels of creep strain accumulated during different dwells, following loading from different macroscopic stress states, is also evaluated and explained through experimental observations. Assessing the impact of displacement- and load-controlled dwells on the hysteresis loop evolution revealed how different assumptions for creep strain accumulation could employ SCM results to reduce uncertainty. Guidance is provided in the development of extrapolation frameworks for structural parameters based on the SCM results which could be used directly in structural assessment to estimate the inelastic strain accumulation with a lower degree of conservatism.

A CCM-based modular and hybrid kinetic model to simulate the tryptophan synthesis in a fed-batch bioreactor using modified E. coli cells

This paper presents a novel methodology to develop complex structured bi-level (hybrid) kinetic models of some important cell bioprocesses. The approached case study refers to the tryptophan (TRP) synthesis in E. coli cells. The required experimental data were collected from a fed-batch bioreactor (FBR). The developed structured kinetic model includes several inter-connected reaction pathway (modules) able to simulate the kinetics of glycolysis, TRP-operon expression, ATP-recovery system, all belonging to the central carbon metabolism (CCM). Experiments were carried out by using an E. coli strain modified to replace the PTS-system with a more efficient one to uptake the glucose (GLC) from the environment. By linking the FBR macroscopic state variables with the nano-scale variables describing the cell metabolic processes of interest, the resulted structured hybrid dynamic model presents a large number of advantages, such as: allows further in-silico (model-based) more accurate engineering developments; it can be used for bioinformatics purposes.

Machine Learning Integration With Combustion Physics to Develop a Composite Predictive Model for Reactivity Controlled Compression Ignition Engine

Abstract Phasing of combustion metrics close to the optimum values across operation range is necessary to avail benefits of reactivity controlled compression ignition (RCCI) engines. Parameters like start of combustion occurrence crank angle (CA) (θsoc), occurrence of burn rate fraction reaching 50% (θ50), mean effective pressure from indicator diagram (IMEP), etc. are described as combustion metrics. These metrics act as markers for the macroscopic state of combustion. Control of these metrics in RCCI engine is relatively complex due to the nature of ignition. As direct combustion control is challenging, alternative methods like combustion physics-derived models are a subject of research interest. In this work, a composite predictive model was proposed by integrating trained random forest (RF) machine learning and artificial neural networks (ANNs) to combustion physics-derived modified Livengood–Wu integral, parametrized double-Wiebe function, autoignition front propagation speed-based correlations, and residual gas fraction model. The RF machine learning established a correlative relationship between physics-based model coefficients and engine operating condition. The ANN developed a similar correlation between residual gas fraction parameters and engine operating condition. The composite model was deployed for the predictions of θsoc, θ50, and IMEP as RCCI engine combustion metrics. Experimental validation showed an error standard deviation (σ68.3,err) of 0.67°CA, 1.19°CA, 0.223 bar and symmetric mean absolute percentage error of 6.92%, 7.87%, and 4.01% for the predictions of θsoc, θ50, and IMEP, respectively, on cycle to cycle basis. Wide range applicability, lesser experiments for model calibration, low computational costs, and utility for control applications were the benefits of the proposed predictive model.

Artificial out-of-plane Ising antiferromagnet on the kagome lattice with very small farther-neighbor couplings

Despite their simple formulation, short range classical antiferromagnetic Ising models on frustrated lattices give rise to exotic phases of matter, in particular due to their macroscopic ground state degeneracy. Recent experiments on artificial spin systems comprising arrays of chirally coupled nanomagnets provide a significant strengthening of the nearest neighbour couplings compared to systems with dipolar-coupled nanomagnets. This opens the way to design artificial spin systems emulating Ising models with nearest neighbour couplings. In this paper, we compare the results of an extensive investigation with tensor network and Monte Carlo simulations of the nearest- and further-neighbour ($J_1-J_2-J_{3||}$) kagome Ising antiferromagnet with the experimental spin-spin correlations of a kagome lattice of chirally coupled nanomagnets. Even though the ratios between the further neighbour couplings and the nearest neighbour coupling estimated from micromagnetic simulations are much smaller than for dipolar-coupled nanomagnets, we show that they still play an essential role in the selection of the correlations.

Feedback-induced desynchronization and oscillation quenching in a population of globally coupled oscillators.

Motivated from a wide range of applications, various methods to control synchronization in coupled oscillators have been proposed. Previous studies have demonstrated that global feedback typically induces three macroscopic behaviors: synchronization, desynchronization, and oscillation quenching. However, analyzing all of these transitions within a single theoretical framework is difficult, and thus the feedback effect is only partially understood in each framework. Herein, we analyze a model of globally coupled phase oscillators exposed to global feedback, which shows all of the typical macroscopic dynamical states. Analytical tractability of the model enables us to obtain detailed phase diagrams where transitions and bistabilities between different macroscopic states are identified. Additionally, we propose strategies to steer the oscillators into targeted states with minimal feedback strength. Our study provides a useful overview of the effect of global feedback and is expected to serve as a benchmark when more sophisticated feedback needs to be designed.

Adaptive signal control for bus service reliability with connected vehicle technology via reinforcement learning

This paper presents an adaptive signal controller for managing traffic delays and urban bus service reliability with fully adaptable acyclic timing plans. The signal controller is built upon a reinforcement learning framework that consists of a model-based and a data-driven component. The model-based component is represented by a hybrid kinematic wave traffic model that integrates macroscopic flow-based and microscopic vehicle-based state variables subject to stochastic demands and bus service status. To cope with the high dimensional solution space, the data-driven component is incorporated as a multi-layer artificial neural network and is used to approximate future traffic states and system performances with respect to prevailing control settings. Before the controller can be used, the neural network is to be trained through a series of realised dynamic state transitions via an on-policy temporal difference learning algorithm. The proposed control framework is tested over a real world corridor scenario in London, UK. The proposed controller is able to reduce both traffic delays and bus service variabilities subject to stochastic demands with acyclic timing plans that can be derived in short computational time. This study contributes to the design of adaptive network traffic control for multi-modal networks with connected vehicle technology and advanced learning-based optimisation techniques.

Quantum tricritical behavior and multistable macroscopic quantum states in generalized Dicke model

In this article, we obtain the multistable macroscopic quantum states and quantum tricritical behavior in generalized Dicke model (DM) analytically by means of spin-coherent-state (SCS) variational method for a finite atom number N. The quantum phase transition (QPT) is characterized by the ground state from the normal phase (NP) to the superradiant phases (SP). The second-order QPT turns to the first-order one at the tricritical point of phase boundary. The tricritical behavior of QPT emerges when the coupling strength of the symmetry breaking mechanism increases to a certain extent, which leads to an additional dimension of phase diagram. Beyond the ground state the symmetry-breaking coupling can manipulate the pseudospin flip of higher-level macroscopic states. The critical property of QPT is also demonstrated from the average photon-number, the atomic population as well as Berry phase.

Magnon-atom interaction via dispersive cavities: Magnon entanglement

In general, a magnon does not interact directly with an atom. Here we present a scheme to generate coherent coupling between two magnons and a superconducting artificial atom via exchange of virtual photons in dispersive cavities. By probing into the induced magnon-atom interactions, we identify the two-magnon process, which causes almost perfect two-mode squeezing and entanglement of the two magnons for specific conditions. The quantum entanglement of the magnon modes in two massive ferrimagnets survives at the steady state and represents genuinely macroscopic quantum state. Our work offers an interesting alternative in designing experiments for generating the coherent coupling between the magnons and the atom. It finds broad applications in the investigation of macroscopic quantum effects and the preparation of quantum devices.

Cut-Through Fractured Seepage Properties and Numerical Simulation of Sandstone after Different Temperature Treatments

To explore the seepage characteristics of cut-through fractured rocks after different temperatures, sandstone in the Hunan area was selected as the research object. First, the influence degree of different temperatures on the permeability of fractured sandstone was studied, and the permeability variation of fractured sandstone with net confining pressure was revealed. The test data was nonlinearly fitted to prove that the relationship between permeability and net confining pressure conforms to the characteristics of the negative exponential function. Second, the macroscopic fractured state of sandstone after different temperature treatments was analyzed, and it is concluded that the inclination angle of the fracture surface decreases with the applied thermal temperature, the fracture surface gradually develops into a single shear failure surface, and the damage degree becomes more and more serious. Finally, the theoretical formula for the calculation of fractured seepage was introduced, and the FLAC3D embedded fish language was used to compile the seepage-stress coupling calculation program of the fractured sandstone after different temperature treatments. Numerical calculations were carried out based on samples with different fracture angles of fractured sandstone, and the calculated values were in good agreement with the test results. The research results can provide guiding significance for the research on the influence of high temperature in fire tunnel on the evolution of permeability of surrounding rock fissures.

The macroscopic quantum state of ion channels: A carrier of neural information

Neuroscience has been extensively developed for more than seventy years; however, there still remains lack of breakthrough understanding in the neural information field. A quantum coherent state of ion channels is recently proposed in neural system as an information carrier, but its physical expression is still not fixed. Here, employing a simple K+ channel as a typical instance, we theoretically build a conceptual model for the quantum state of a neural ion channel and demonstrate the macroscopic coherence state of multiple ion channels. The underlying mechanism is revealed to the mid-infrared photons released by ion oscillation in one ion channel together with their resonant and coherent coupling with the oscillations in other channels. An additional environment field (e.g., brain wave) may regulate the coherence, potentially relating to the human consciousness. Clearly, there exist the channels of other ions in a neural system, which also potentially form a macroscopic coherence state of themselves, with the mechanism identical to that of K+ channels. An environment field can be expected to further regulate the quantum states of various ionic channels, leading to a total macroscopic coherence state of these channels, which is like the conductor in a symphony to achieve the harmony in music. These findings are expected to provide a promising viewpoint for neuroscience, as well as to improve treatments of the diseases and health problems related to neural system.

Azimuthal eigenmodes at strongly non-degenerate parametric down-conversion

Quantum-optical technologies based on parametric light down-conversion are not yet applied in the terahertz frequency range. This is owing to the complexity of detecting single photons in the terahertz frequency range and the strong entanglement of modes of optical-terahertz biphotons. This study investigates the angular structure of scattered radiation generated by strongly non-degenerate parametric down-conversion when the frequency of the idler radiation does not exceed several terahertz. We demonstrate that under certain approximations, it is possible to obtain azimuthal eigenmodes for the nonlinear-interaction operator. The solution of the evolution equations for the field operators in these eigenmodes has the form of the Bogolyubov transformation, which allows a scattering matrix to be obtained for arbitrary values of the parametric gain. This scattering matrix describes both the production of biphoton pairs and the generation of intense fluxes of correlated optical-terahertz fields that form a macroscopic quantum state of radiation in two spectral ranges.

Thermo-mechanical Transport in Rotor Chains

We study the macroscopic profiles of temperature and angular momentum in the stationary state of chains of rotors under a thermo-mechanical forcing applied at the boundaries. These profiles are solutions of a system of diffusive partial differential equations with boundary conditions determined by the thermo-mechanical forcing. Instead of expensive Monte Carlo simulations of the underlying microscopic dynamics, we perform extensive numerical computations based on a finite difference method for the system of partial differential equations describing the macroscopic steady state. We first present a formal derivation of these stationary equations based on a linear response argument and local equilibrium assumptions. We then study various properties of the solutions to these equations. This allows to characterize the regime of parameters leading to uphill energy diffusion -- a situation in which the energy flows in the direction of the gradient of temperature -- and to identify regions of parameters corresponding to a negative energy conductivity (i.e. a positive linear response of the energy current to a gradient of temperature). The macroscopic equations we derive are consistent with some previous results obtained by numerical simulation of the microscopic physical system, which confirms their validity.

3D architected isotropic materials with tunable stiffness and buckling strength

This paper presents a class of 3D single-scale isotropic materials with tunable stiffness and buckling strength obtained via topology optimization and subsequent shape optimization. Compared to stiffness-optimal closed-cell plate material, the material class reduces the Young’s modulus to a range from 79% to 58%, but improves the uniaxial buckling strength to a range from 180% to 767%. Based on small deformation theory, material stiffness is evaluated using the homogenization method. Buckling strength under a given macroscopic stress state is estimated using linear buckling analysis with Block–Floquet boundary conditions to capture both short and long wavelength buckling modes. The 3D isotropic single-scale materials with tunable properties are designed using topology optimization, and are then further simplified using shape optimization. Both topology and shape optimized results demonstrate that material buckling strength can be significantly enhanced by hybrids between truss and variable thickness plate structures.

A variational-based homogenization model for plastic shakedown analysis of porous materials with a large range of porosity

In this paper, a new homogenization model is developed for the determination of macroscopic shakedown limit state for porous media subjected to general cyclic loads. Based on the variational principle over the stabilized cycle, a new macroscopic fatigue criterion is established, beyond which the collapse of material will occur due to fatigue. Unlike the classical Melan’s theory by statical approaches relying on the complex construction of a time-independent residual stress field, the formulation of the new criterion is derived from a variational-based kinematical approach which allows overcoming this difficulty. Dirac’s measure is adopted to simplify the volume integral with the assumption of vanishing plastic strain increment over a stabilized cycle. The established criteria exhibit directly the dependence of the plastic shakedown limit load on the invariants of the macroscopic stress tensor, Poisson’s ratio of the solid matrix and the porosity. The macroscopic safety domain is defined by the intersection of limit surface of this new fatigue criteria and that corresponds to the incremental collapse which is reached monotonically. The theoretical predictions of shakedown limits by the new criterion are compared with the numerical results obtained from non-linear optimization method and with those given by some representative existing criteria. It is shown that the new criterion significantly improves the accuracy of plastic shakedown limit prediction for porous materials with large values of porosity.

Local detailed balance across scales: From diffusions to jump processes and beyond.

Diffusive dynamics in presence of deep energy minima and weak nongradient forces can be coarse grained into a mesoscopic jump process over the various basins of attraction. Combining standard weak-noise results with a path integral expansion around equilibrium, we show that the emerging transition rates satisfy local detailed balance (LDB). Namely, the log ratio of the transition rates between nearby basins of attractions equals the free-energy variation appearing at equilibrium, supplemented by the work done by the nonconservative forces along the typical transition path. When the mesoscopic dynamics possesses a large-size deterministic limit, it can be further reduced to a jump process over macroscopic states satisfying LDB. The persistence of LDB under coarse graining of weakly nonequilibrium states is a generic consequence of the fact that only dissipative effects matter close to equilibrium.

Membrane transporter dimerization driven by differential lipid solvation energetics of dissociated and associated states.

Over two-thirds of integral membrane proteins of known structure assemble into oligomers. Yet, the forces that drive the association of these proteins remain to be delineated, as the lipid bilayer is a solvent environment that is both structurally and chemically complex. In this study, we reveal how the lipid solvent defines the dimerization equilibrium of the CLC-ec1 Cl-/H+ antiporter. Integrating experimental and computational approaches, we show that monomers associate to avoid a thinned-membrane defect formed by hydrophobic mismatch at their exposed dimerization interfaces. In this defect, lipids are strongly tilted and less densely packed than in the bulk, with a larger degree of entanglement between opposing leaflets and greater water penetration into the bilayer interior. Dimerization restores the membrane to a near-native state and therefore, appears to be driven by the larger free-energy cost of lipid solvation of the dissociated protomers. Supporting this theory, we demonstrate that addition of short-chain lipids strongly shifts the dimerization equilibrium toward the monomeric state, and show that the cause of this effect is that these lipids preferentially solvate the defect. Importantly, we show that this shift requires only minimal quantities of short-chain lipids, with no measurable impact on either the macroscopic physical state of the membrane or the protein's biological function. Based on these observations, we posit that free-energy differentials for local lipid solvation define membrane-protein association equilibria. With this, we argue that preferential lipid solvation is a plausible cellular mechanism for lipid regulation of oligomerization processes, as it can occur at low concentrations and does not require global changes in membrane properties.

Data-driven reduced homogenization for transient diffusion problems with emergent history effects

In this paper, we propose a data-driven reduced homogenization technique to capture diffusional phenomena in heterogeneous materials which reveal, on a macroscopic level, a history-dependent non-Fickian behavior. The adopted enriched-continuum formulation, in which the macroscopic history-dependent transient effects are due to the underlying heterogeneous microstructure is represented by enrichment-variables that are obtained by a model reduction at the micro-scale. The data-driven reduced homogenization minimizes the distance between points lying in a data-set and points associated with the macroscopic state of the material. The enrichment-variables are excellent pointers for the selection of the correct part of the data-set for problems with a time-dependent material state. Proof-of-principle simulations are carried out with a heterogeneous linear material exhibiting a relaxed separation of scales. Information obtained from simulations carried out at the micro-scale on a unit-cell is used to determine approximate values of metric coefficients in the distance function. The proposed data-driven reduced homogenization also performs adequately in the case of noisy data-sets. Finally, the possible extensions to non-linear history-dependent behavior are discussed.