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The thermodynamics and microstructure of confined fluids with small particle number are best described using the canonical ensemble. However, practical calculations can usually only be performed in the grand-canonical ensemble, which can introduce unphysical artifacts. We employ the method of asymptotics to transform grand-canonical observables to the canonical ensemble, where the former can be conveniently obtained using the classical density functional theory of inhomogeneous fluids. By formulating the ensemble transformation as a contour integral in the complex fugacity plane we reveal the influence of the Yang-Lee zeros in determining the form and convergence properties of the asymptotic series. The theory is employed to develop expansions for the canonical partition function and the canonical one-body density. Numerical investigations are then performed using an exactly soluble one-dimensional model system of hard-rods.

We report on precision spectroscopy of the 6s$^2$ $^1$S$_0\to$6s6p $^3$P$_1$ intercombination line of mercury in the deep ultraviolet, by means of a frequency-comb referenced, wavelength-modulated, saturated absorption technique. This method allowed us to perform sub-Doppler investigations with an absolute frequency axis at 254 nm, while ensuring a relatively high signal-to-noise ratio. The absolute line center frequencies of the $^{200}$Hg and $^{202}$Hg bosonic isotopes were measured with a global uncertainty of 8 and 15 kHz (namely, 6.8$\times$10$^{-12}$ and 1.3$\times$10$^{-11}$, in relative terms), respectively, the statistical and systematic components being significantly reduced as compared to past determinations. This remarkable result was achieved also thanks to an in-depth study of the AC stark effect. Furthermore, we found the most accurate $^{200}$Hg-$^{202}$Hg isotope shift ever obtained before, namely, $5295570\pm15_{stat}\pm8_{syst}$ kHz.

We present a decentralized reinforcement learning (RL) approach to address the multi-agent shepherding control problem, departing from the conventional assumption of cohesive target groups. Our two-layer control architecture consists of a low-level controller that guides each herder to contain a specific target within a goal region, while a high-level layer dynamically selects from multiple targets the one an herder should aim at corralling and containing. Cooperation emerges naturally, as herders autonomously choose distinct targets to expedite task completion. We further extend this approach to large-scale systems, where each herder applies a shared policy, trained with few agents, while managing a fixed subset of agents.

In this paper, we address the large-scale shepherding control problem using a continuification-based strategy. We consider a scenario in which a large group of follower agents (targets) must be confined within a designated goal region through indirect interactions with a controllable set of leader agents (herders). Our approach transforms the microscopic agent-based dynamics into a macroscopic continuum model via partial differential equations (PDEs). This formulation enables efficient, scalable control design for the herders' behavior, with guarantees of global convergence. Numerical and experimental validations in a mixed-reality swarm robotics framework demonstrate the method's effectiveness.

We present GoRINNs: numerical analysis-informed (shallow) neural networks for the solution of inverse problems of non-linear systems of conservation laws. GoRINNs is a hybrid/blended machine learning scheme based on high-resolution Godunov schemes for the solution of the Riemann problem in hyperbolic Partial Differential Equations (PDEs). In contrast to other existing machine learning methods that learn the numerical fluxes or just parameters of conservative Finite Volume methods, relying on deep neural networks (that may lead to poor approximations due to the computational complexity involved in their training), GoRINNs learn the closures of the conservation laws per se based on "intelligently" numerical-assisted shallow neural networks. Due to their structure, in particular, GoRINNs provide explainable, conservative schemes, that solve the inverse problem for hyperbolic PDEs, on the basis of approximate Riemann solvers that satisfy the Rankine-Hugoniot condition. The performance of GoRINNs is assessed via four benchmark problems, namely the Burgers', the Shallow Water, the Lighthill-Whitham-Richards and the Payne-Whitham traffic flow models. The solution profiles of these PDEs exhibit shock waves, rarefactions and/or contact discontinuities at finite times. We demonstrate that GoRINNs provide a very high accuracy both in the smooth and discontinuous regions., Comment: 29 pages, 6 figures

The strong electron-electron interaction in transition metal dichalcogenides (TMDs) gives rise to phenomena such as strong exciton and trion binding and excitonic condensation, as well as large negative exchange and correlation contributions to the electron energies, resulting in negative electronic compressibility. Here we use angle-resolved photoemission spectroscopy to demonstrate a striking effect of negative electronic compressibility in semiconducting TMD MoS2 on the charge transfer to and from a partial overlayer of monolayer semimetallic WTe2. We track the changes in binding energy of the valence bands of both WTe2 and MoS2 as a function of surface transfer doping with donor (K) and acceptor (F4-TCNQ) species. Donor doping increases the binding energy of the MoS2 valence band, as expected, while counterintuitively reducing the binding energy of the WTe2 valence bands and core levels. The inverse effect is observed for acceptor doping, where a typical reduction in the MoS2 binding energies is accopanied by an unexpected increase in those of WTe2. The observations imply a reversal of the expected charge transfer; donor (acceptor) deposition decreases (increases) the carrier density in the WTe2 adlayer. The charge transfer reversal is a direct consequence of the negative electronic compressibility of the MoS2 surface layer, for which addition (subtraction) of charge leads to attraction (repulsion) of further charge from neighbouring layers. These findings highlight the importance of many-body interactions for the electrons in transition metal dichalcogenides and underscore the potential for exploring strongly correlated quantum states in two-dimensional semiconductors., Comment: 4 pages, 5 figures + supplementary information

The Berry phase is a fundamental concept in quantum mechanics with profound implications for understanding topological properties of quantum systems. This tutorial provides a comprehensive introduction to the Berry phase, beginning with the essential mathematical framework required to grasp its significance. We explore the intrinsic link between the emergence of a non-trivial Berry phase and the presence of topological characteristics in quantum systems, showing the connection between the Berry phase and the band structure as well as the phase's gauge-invariant nature during cyclic evolutions. The tutorial delves into various topological effects arising from the Berry phase, such as the quantum, anomalous, and spin Hall effects, which exemplify how these quantum phases manifest in observable phenomena. We then extend our discussion to cover the transport properties of topological insulators, elucidating their unique behaviour rooted in Berry phase physics. This tutorial aims at equipping its readers with a robust understanding of the basic theory around the Berry phase and its pivotal role in the realm of topological quantum phenomena., Comment: 24 pages, 11 figures

As interactions with autonomous agents-ranging from robots in physical settings to avatars in virtual and augmented realities-become more prevalent, developing advanced cognitive architectures is critical for enhancing the dynamics of human-avatar groups. This paper presents a reinforcement-learning-based cognitive architecture, trained via a sim-to-real approach, designed to improve synchronization in periodic motor tasks, crucial for applications in group rehabilitation and sports training. Extensive numerical validation consistently demonstrates improvements in synchronization. Theoretical derivations and numerical investigations are complemented by preliminary experiments with real participants, showing that our avatars can integrate seamlessly into human groups, often being indistinguishable from humans.

We address the problem of controlling the density of a large ensemble of follower agents by acting on a group of leader agents that interact with them. Using coupled partial integro-differential equations to describe leader and follower density dynamics, we establish feasibility conditions and develop two control architectures ensuring global stability. The first employs feed-forward control on the followers' and a leaders' density feedback. The second implements a dual feedback loop through a reference-governor that adapts the leaders' density based on both populations measurements. Our methods, initially developed in a one-dimensional setting, are extended to multi-dimensional cases, and validated through numerical simulations for representative control applications, both for groups of infinite and finite size.

Logic circuits consist of devices that can be controlled between two distinct states. The recent demonstration that a superconducting current flowing in a constriction can be controlled via a gate voltage ($V_G$) - can lead to superconducting logic with better performance than existing logics. However, before such logic is developed, high reproducibility in the functioning of GCS devices and optimization of their performance must be achieved. Here, we report an investigation of gated Nb devices showing GCS with unprecedently-high reproducibility. Based on the investigation of a statistically-significant number of devices, we demonstrate that the GCS is independent of the constriction width, in contrast with previous reports, and confirm a strong correlation between the GCS and the leakage current ($I_{leak}$) induced by $V_G$. We also achieve a voltage output in our devices larger than the typical values reported to date by at least one order of magnitude, which is relevant for the future interconnection of devices, and show that $I_{leak}$ can be used as a tool to modulate the operational $V_G$ of devices on a $SiO_2$ substrates. These results altogether represent an important step forward towards the optimization of reproducibility and performance of GCS devices, and the future development of a GCS-based logic., Comment: 31 pages, 14 figures, 1 table

This paper investigates the robustness of a novel high-dimensional continuification control method for complex multi-agent systems. We begin by formulating a partial differential equation describing the spatio-temporal density dynamics of swarming agents. A stable control action for the density is then derived and validated under nominal conditions. Subsequently, we discretize this macroscopic strategy into actionable velocity inputs for the system's agents. Our analysis demonstrates the robustness of the approach beyond idealized assumptions of unlimited sensing and absence of perturbations., Comment: arXiv admin note: text overlap with arXiv:2310.01573

We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.

Materials with strongly correlated electrons exhibit physical properties that are often difficult to predict as they result from the interactions of large numbers of electrons combined with several quantum degrees of freedom. The layered oxide perovskite Sr$_2$RuO$_4$ is a strongly correlated electron material that has been intensively investigated since its discovery due to its unusual physical properties. Whilst recent experiments have reopened the debate on the exact symmetry of the superconducting state in Sr$_2$RuO$_4$, a deeper understanding of the Sr$_2$RuO$_4$ normal state appears crucial as this is the background in which electron pairing occurs. Here, by using low-energy muon spin spectroscopy we discover the existence of magnetism at the surface of Sr$_2$RuO$_4$ in its normal state. We detect static weak dipolar fields yet manifesting below a relatively high onset temperature larger than 50 K, which reveals the unconventional nature of the observed magnetism. We relate the origin of this phase breaking time reversal symmetry to electronic ordering in the form of orbital loop currents that originate at the reconstructed Sr$_2$RuO$_4$ surface. Our observations set a reference for the discovery of the same magnetic phase in other materials and unveil an electronic ordering mechanism that can influence unconventional electron pairing with broken time reversal symmetry in those materials where the observed magnetic phase coexists with superconductivity., Comment: 20 pages, 5 figures

Identifying superconducting materials with spin-polarized Cooper pairs is an important objective both for exploration of new fundamental physics and for cryogenic applications in spintronics and quantum sensing. We here compute the critical temperature $T_c$ of the superconducting transition in a bilayer comprised of a superconductor with an intrinsic spin-triplet order parameter and a ferromagnet. We determine how $T_c$ varies both with the thickness of the ferromagnet and its magnetization direction. We show that both the orbital and spin part of the triplet superconducting order parameter leave clear signatures in $T_c$ which do not appear in a bilayer of a conventional s-wave superconductor and a ferromagnet. In particular, the dependence of $T_c$ on these variables changes depending on whether or not the superconducting order parameter features Andreev bound-states and also changes qualitatively when the magnetization is rotated in the plane of the ferromagnetic film. Measurements of $T_c$ in such bilayers are therefore useful to identify the pairing symmetry of intrinsic triplet superconductors.

A key problem toward the use of microorganisms as bio-factories is reaching and maintaining cellular communities at a desired density and composition so that they can efficiently convert their biomass into useful compounds. Promising technological platforms for the real time, scalable control of cellular density are bioreactors. In this work, we developed a learning-based strategy to expand the toolbox of available control algorithms capable of regulating the density of a \textit{single} bacterial population in bioreactors. Specifically, we used a sim-to-real paradigm, where a simple mathematical model, calibrated using a few data, was adopted to generate synthetic data for the training of the controller. The resulting policy was then exhaustively tested in vivo using a low-cost bioreactor known as Chi.Bio, assessing performance and robustness. In addition, we compared the performance with more traditional controllers (namely, a PI and an MPC), confirming that the learning-based controller exhibits similar performance in vivo. Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia., Comment: 13 pages, 4 figures

The application of a gate voltage to control the superconducting current flowing through a nanoscale superconducting constriction, named as gate-controlled supercurrent (GCS), has raised great interest for fundamental and technological reasons. To gain a deeper understanding of this effect and develop superconducting technologies based on it, the material and physical parameters crucial for GCS must be identified. Top-down fabrication protocols should be also optimized to increase device scalability, although studies suggest that top-down fabricated devices are more resilient to show GCS. Here, we investigate gated superconducting nanobridges made with a top-down fabrication process from thin films of the non-centrosymmetric superconductor NbRe. Unlike other devices previously reported, our NbRe devices systematically exhibit GCS, when made in specific conditions, which paves the way for higher device scalability. Our results also suggest that surface properties of NbRe nanobridges and their modification during fabrication are key for GCS., Comment: 14 pages, 4 figures

Here we demonstrate superconducting Dayem-bridge weak-links made of different stoichiometric compositions of NbRe. Our devices possess a relatively high critical temperature, normal-state resistance, and kinetic inductance. In particular, the high kinetic inductance makes this material a good alternative to more conventional niobium-based superconductors (e.g., NbN or NbTiN) for the realization of superinductors and high-quality factor resonators, whereas the high normal-state resistance yields a large output voltage in superconducting switches and logic elements realized upon this compound. Moreover, out-of-plane critical magnetic fields exceeding 2 T ensure that possible applications requiring high magnetic fields can also be envisaged. Altogether, these features make this material appealing for a number of applications in the framework of quantum technologies., Comment: 10 pages, 4 figures

In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment. Motivated by this necessity, we present a set of results and a systematic reward shaping procedure that (i) ensures the optimal policy generates trajectories that align with specified control requirements and (ii) allows to assess whether any given policy satisfies them. We validate our approach through comprehensive numerical experiments conducted in two representative environments from OpenAI Gym: the Inverted Pendulum swing-up problem and the Lunar Lander. Utilizing both tabular and deep reinforcement learning methods, our experiments consistently affirm the efficacy of our proposed framework, highlighting its effectiveness in ensuring policy adherence to the prescribed control requirements.

Using first-principle calculations, we investigate the electronic, topological and superconducting properties of Nb$_3$X (X = Ge, Sn, Sb) and Ta$_3$Y (Y = As, Sb, Bi) A15 compounds. We demonstrate that these compounds host Dirac surface states which are related to a nontrivial Z$_2$ topological value. The spin-orbit coupling (SOC) splits the eightfold degenerate R point close to the Fermi level enhancing the amplitude of the spin Hall conductance. Indeed, despite the moderate spin-orbit of the Nb-compounds, a large spin Hall effect is also obtained in Nb$_3$Ge and Nb$_3$Sn compounds. We show that the Coulomb interaction opens the gap at the R point thus making more evident the occurrence of Dirac surface states. We then investigate the superconducting properties by determining the strength of the electron-phonon BCS coupling. The evolution of the critical temperature is tracked down to the 2D limit indicating a reduction of the transition temperature which mainly arises from the suppression of the density of states at the Fermi level. Finally, we propose a minimal tight-binding model based on three coupled Su-Schrieffer-Heeger chains with t$_{2g}$ Ta- and Nb-orbitals reproducing the spin-orbit splittings at the R point among the $\pi$-bond bands in this class of compounds. We separate the kinetic parameters in $\pi$ and $\delta$-bonds, in intradimer and interdimer hoppings and discuss their relevance for the topological electronic structure. We point out that Nb$_3$Ge might represent a Z$_2$ topological metal with the highest superconducting temperature ever recorded., Comment: 16 pages, 12 figures in main text, 3 figures in appendix, Paper submitted to Physical Review B

Many new methodologies for the control of large-scale multi-agent systems are based on macroscopic representations of the emerging system dynamics, in the form of continuum approximations of large ensembles. These techniques, that are developed in the limit case of an infinite number of agents, are usually validated only through numerical simulations. In this paper, we introduce a mixed reality set-up for testing swarm robotics techniques, focusing on the macroscopic collective motion of robotic swarms. This hybrid apparatus combines both real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We also extend continuification-based control methods for swarms to higher dimensions, and assess experimentally their validity in the new platform. Our study demonstrates the effectiveness of the platform for conducting large-scale swarm robotics experiments, and it contributes new theoretical insights into control algorithms exploiting continuification approaches.

We study Y3Fe5O12 (YIG) flakes that we produce via mechanical cleaving and exfoliation of YIG single crystals. By characterizing their structural and magnetic properties, we find that these YIG nanoflakes have surfaces oriented along unusual crystallographic axes and uniaxial in-plane magnetic anisotropy due to their shape, both of which are not commonly available in YIG thin films. These physical properties, combined with the possibility of picking up the YIG flakes and stacking them onto flakes of other van der Waals materials or pre-patterned electrodes or waveguides, open unexplored possibilities for magnonics and for the realization of novel YIG-based heterostructures and devices., Comment: 13 pages, 4 figures

We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization. When traditional optimization methods are inapplicable, due to uncertain or unknown node dynamics, we propose a data-driven approach leveraging datasets of relevant examples. We analyze two case studies, with linear and nonlinear node dynamics. First, we show how including node dynamics in the objective function makes the optimal graphs heterogeneous. Then, we compare various design strategies, finding that the best either utilize data samples close to a specific Pareto front or a combination of a neural network and a genetic algorithm, with statistically better performance than the best examples in the datasets.

We study the shepherding control problem where a group of "herders" need to orchestrate their collective behaviour in order to steer the dynamics of a group of "target" agents towards a desired goal. We relax the strong assumptions of targets showing cohesive collective behavior in the absence of the herders, and herders owning global sensing capabilities. We find scaling laws linking the number of targets and minimum herders needed, and we unveil the existence of a critical threshold of the density of the targets, below which the number of herders needed for success significantly increases. We explain the existence of such a threshold in terms of the percolation of a suitably defined herdability graph and support our numerical evidence by deriving and analysing a PDE describing the herders dynamics in a simplified one-dimensional setting. Extensive numerical experiments validate our methodology.

Chalcogen vacancies in transition metal dichalcogenides are widely acknowledged as both donor dopants and as a source of disorder. The electronic structure of sulphur vacancies in MoS2 however is still controversial, with discrepancies in the literature pertaining to the origin of the in-gap features observed via scanning tunneling spectroscopy (STS) on single sulphur vacancies. Here we use a combination of scanning tunnelling microscopy (STM) and STS to study embedded sulphur vacancies in bulk MoS2 crystals. We observe spectroscopic features dispersing in real space and in energy, which we interpret as tip position- and bias-dependent ionization of the sulphur vacancy donor due to tip induced band bending (TIBB). The observations indicate that care must be taken in interpreting defect spectra as reflecting in-gap density of states, and may explain discrepancies in the literature., Comment: 7 pages, 5 figures

The combination of a superconductor with a magnetically inhomogeneous material has been established as an efficient mechanism for the generation of long-ranged spin-polarized (spin-triplet) Cooper pairs. Evidence for this mechanism, however, has been established based on studies done on three-dimensional systems, where the strong bonds existing at the interface between the superconductor and the magnetic material should in principle enhance proximity effects and strengthen any electronic correlations. Here, we fabricate devices based on van der Waals stacks of flakes of the two-dimensional superconductor $NbS_2$ combined with flakes of $Cr_{1/3}NbS_2$, which has a built-in magnetic inhomogeneity due to its helimagnetic spin texture at low temperatures. We find that the critical temperature of these vdW bilayers is strongly dependent on the magnetic state of $Cr_{1/3}NbS_2$, whose degree of magnetic inhomogeneity can be controlled via an applied magnetic field. Our results demonstrate evidence for the generation of long-ranged spin-triplet pairs across the $Cr_{1/3}NbS_2$/$NbS_2$ vdW interface., Comment: 13 pages, 3 figures

We present preliminary results on the problem of driving the dynamics of a group of agents, the herders, so as to steer the collective behaviour of another group of agents, the targets, interacting with them. We define this problem as the multiagent herding control problem and study the herdability of the target agents by relaxing some strong assumptions often made in the existing literature, namely that targets are cohesive in the absence of herders and that herders possess global information. We find that scaling laws exist linking crucial parameters such as the herders' sensing radius and the targets density with the herdability of the system. Our most surprising observation, supported by exhaustive numerical experiments, is that the crucial effect of limited sensing is that the minimum number of herders required to solve the problem significantly increases below a critical minimum density of the target agents., Comment: An updated version is available at arXiv:2307.16797

The control of a superconducting current via the application of a gate voltage has been recently demonstrated in a variety of superconducting devices. Although the mechanism underlying this gate-controlled supercurrent (GCS) effect remains under debate, the GCS effect has raised great interest for the development of the superconducting equivalent of conventional metaloxide semiconductor electronics. To date, however, the GCS effect has been mostly observed in superconducting devices made by additive patterning. Here, we show that devices made by subtractive patterning show a systematic absence of the GCS effect. Doing a microstructural analysis of these devices and comparing them to devices made by additive patterning, where we observe a GCS, we identify some material and physical parameters that are crucial for the observation of a GCS. We also show that some of the mechanisms proposed to explain the origin of the GCS effect are not universally relevant., Comment: 19 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2302.13734

We investigate the stability and robustness properties of a continuification-based strategy for the control of large-scale multiagent systems. Within continuation-based strategy, one transforms the microscopic, agent-level description of the system dynamics into a macroscopic continuum-level, for which a control action can be synthesized to steer the macroscopic dynamics towards a desired distribution. Such an action is ultimately discretized to obtain a set of deployable control inputs for the agents to achieve the goal. The mathematical proof of convergence toward the desired distribution typically relies on the assumptions that no disturbance is present and that each agent possesses global knowledge of all the others' positions. Here, we analytically and numerically address the possibility of relaxing these assumptions for the case of a one-dimensional system of agents moving in a ring. We offer compelling evidence in favor of the use of a continuification-based strategy when agents only possess a finite sensing capability and spatio-temporal perturbations affect the macroscopic dynamics of the ensemble. We also discuss some preliminary results about the role of an integral action in the macroscopic control solution.

Geometric pattern formation is an important emergent behavior in many applications involving large-scale multi-agent systems, such as sensor networks deployment and collective transportation. Attraction/repulsion virtual forces are the most common control approach to achieve such behavior in a distributed and scalable manner. Nevertheless, for most existing solutions only numerical and/or experimental evidence of their convergence is available. Here, we revisit the problem of achieving pattern formation giving sufficient conditions to prove analytically that under the influence of appropriate virtual forces, a large-scale multi-agent swarming system locally converges towards a stable and robust triangular lattice configuration. Specifically, the proof is carried out using LaSalle's invariance principle and geometry-based arguments. Our theoretical results are complemented by exhaustive numerical simulations confirming their effectiveness and estimating the region of asymptotic stability of the triangular configuration.

Recently gate-mediated supercurrent suppression in superconducting nano-bridges has been reported in many experiments. This could be either a direct or an indirect gate effect. The microscopic understanding of this observation is not clear till now. Using the quasiclassical Green's function method, we show that a small concentration of magnetic impurities at the surface of the bridges can significantly help to suppress superconductivity and hence the supercurrent inside the systems while applying a gate field. This is because the gate field can enhance the depairing through the exchange interaction between the magnetic impurities at the surface and the superconductor. We also obtain a \emph{symmetric} suppression of the supercurrent with respect to the gate field, a signature of a direct gate effect. Future experiments can verify our predictions by modifying the surface with magnetic impurities.

The need for faster and more miniaturised electronics is challenging scientists to develop novel forms of electronics based on quantum degrees of freedom different from electron charge. In this fast-developing field, often referred to as quantum electronics, the metal-oxide perovskite SrRuO3 can play an important role thanks to its diverse physical properties, which have been intensively investigated, mostly for conventional electronics. In addition to being chemically stable, easy to fabricate with high quality and to grow epitaxially onto many oxides - these are all desirable properties also for conventional electronics - SrRuO3 has interesting properties for quantum electronics like itinerant ferromagnetism and metallic behaviour, strong correlation between magnetic anisotropy and spin-orbit coupling, strain-tuneable magnetisation, anomalous Hall and Berry effects. In this Research Update, after describing the main phenomena emerging from the interplay between spin, orbital, lattice and topological quantum degrees of freedom in SrRuO3, we discuss the challenges still open to achieve control over these phenomena. We then provide our perspectives on the most promising applications of SrRuO3 for devices for conventional and quantum electronics. We suggest new device configurations and discuss the materials challenges for their realisation. For conventional electronics, we single out applications where SrRuO3 devices can bring competitive advantages over existing ones. For quantum electronics, we propose devices that can help gain a deeper understanding of quantum effects in SrRuO3 to exploit them for quantum technologies. We finally give an outlook about properties of SrRuO3 still waiting for discovery and applications that may stem from them., Comment: 61 pages, 15 figures

In conventional metal-oxide semiconductor (CMOS) electronics, the logic state of a device is set by a gate voltage (VG). The superconducting equivalent of such effect had remained unknown until it was recently shown that a VG can tune the superconducting current (supercurrent) flowing through a nanoconstriction in a superconductor. This gate-controlled supercurrent (GCS) effect can lead to superconducting logics like CMOS logics, but with lower energy dissipation. The physical mechanism underlying the GCS effect, however, remains under debate. In this review article, we illustrate the main mechanisms proposed for the GCS effect, and the material and device parameters that mostly affect it based on the evidence reported. We will come to the conclusion that different mechanisms are at play in the different studies reported so far. We then outline studies that can help answer open questions on the effect and achieve control over it, which is key for applications. We finally give insights into the impact that the GCS effect can have towards high-performance computing with low-energy dissipation and quantum technologies., Comment: 54 pages, 16 figures, 4 tables

Quantum anomalous Hall (QAH) insulators transport charge without resistance along topologically protected chiral one-dimensional edge states. Yet, in magnetic topological insulators (MTI) to date, topological protection is far from robust, with the zero-magnetic field QAH effect only realised at temperatures an order of magnitude below the N\'eel temperature TN, though small magnetic fields can stabilize QAH effect. Understanding why topological protection breaks down is therefore essential to realising QAH effect at higher temperatures. Here we use a scanning tunnelling microscope to directly map the size of the exchange gap (Eg,ex) and its spatial fluctuation in the QAH insulator 5-layer MnBi$_2$Te$_4$. We observe long-range fluctuations of Eg,ex with values ranging between 0 (gapless) and 70 meV, uncorrelated to individual point defects. We directly image the breakdown of topological protection, showing that the chiral edge state, the hallmark signature of a QAH insulator, hybridizes with extended gapless metallic regions in the bulk. Finally, we unambiguously demonstrate that the gapless regions originate in magnetic disorder, by demonstrating that a small magnetic field restores Eg,ex in these regions, explaining the recovery of topological protection in magnetic fields. Our results indicate that overcoming magnetic disorder is key to exploiting the unique properties of QAH insulators.

Polymorphic phases and collective phenomena - such as charge density waves (CDWs) - in transition metal dichalcogenides (TMDs) dictate the physical and electronic properties of the material. Most TMDs naturally occur in a single given phase, but the fine-tuning of growth conditions via methods like molecular beam epitaxy (MBE) allows to unlock otherwise inaccessible polymorphic structures. Exploring and understanding the morphological and electronic properties of new phases of TMDs is an essential step to enable their exploitation in technological applications. Here, we use scanning tunneling microscopy to map MBE-grown monolayer TaTe2. We report the first observation of the 1H polymorphic phase, coexisting with the 1T, and demonstrate that their relative coverage can be controlled by adjusting synthesis parameters. Several super-periodic structures, compatible with CDWs, are observed to coexist on the 1T phase. Finally, we provide theoretical insight on the delicate balance between Te...Te and Ta-Ta interactions that dictates the stability of the different phases. Our findings demonstrate that TaTe2 is an ideal platform to investigate competing interactions, and indicate that accurate tuning of growth conditions is key to accessing metastable states in TMDs.

The problem of partitioning a power grid into a set of islands can be a solution to restore power dispatchment in sections of a grid affected by an extreme failure. Current solutions to this problem usually involve finding the partition of the grid into islands that minimizes the sum of their absolute power imbalances. This combinatorial problem is often solved through heuristic offline methods. In this paper, we propose instead a distributed online algorithm through which nodes can migrate among islands, self-organizing the network into a suitable partition. We prove that, under a set of appropriate assumptions, the proposed solution yields a partition whose absolute power imbalance falls within a given bound of the optimal solution. We validate our analytical results by testing our partitioning strategy on the IEEE 118 and 300 benchmark problems.

Abrikosov Vortices have long been considered as means to encode classical information in low-temperature logic circuits (1) and memory devices (2-4). Although it is possible to control individual vortices using local probes (5-11), scalability towards the control of of multiple vortices remains challenging. Vortex logic devices require means to shuttle selected vortices reliably over long distances between engineered pinning potentials. Concomitantly, all other vortices should remains fixed to their precise locations. Here we demonstrate such capabilities using Nb loops patterned below a NbSe$_2$ layer. SQUID-on-Tip (SOT) microscopy reveals that the loops can position vortices in sites designated to a precision better than 100 nm; they can realize "push" and "pull" operations of vortices as far as 3$\mu$m. Successive application of such operations shuttles a vortex between adjacent loops. Our results may be used as means to integrate vortices in future quantum circuitry. Strikingly, we are able to demonstrate a winding operation. Such winding, if realized in topological superconductors, is considered an essential part of future topological quantum information processing (12-17).

We have designed and constructed a thin film preparation chamber with base pressure of $<2 \times 10^{-9}$~mbar. Currently, the chamber is equipped with two large area evaporators (a molecular evaporator and an electron-beam evaporator), an ion sputtering gun, a thickness monitor and a substrate heater. It is designed such that it can handle large area thin film samples with a future possibility to transfer them in vacuum directly to the low energy muons (LEM) spectrometer or to other advanced characterization facilities in the Quantum Matter and Materials Center (QMMC) which will be constructed in 2024. Initial commissioning of the chamber resulted in high quality, large area and uniform molecular films of CuPc and TbPc$_2$ on various substrate materials. We present first results from low energy $\mu$SR (LE-$\mu$SR) measurements on these films., Comment: 8 pages, 7 figures, muSR2020 conference proceedings

One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous control law, to reduce learning time. The tutor can be designed using an approximate model of the system, without any assumption about the knowledge of the system's dynamics. There is no expectation that it will be able to achieve the control objective if used stand-alone. During learning, the tutor occasionally suggests an action, thus partially guiding exploration. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing. We demonstrate that CT-DQN is able to achieve better or equivalent data efficiency with respect to the classic function approximation solutions.

In this paper, we propose a method to control large-scale multiagent systems swarming in a ring. Specifically, we use a continuification-based approach that transforms the microscopic, agent-level description of the system dynamics into a macroscopic, continuum-level representation, which we employ to synthesize a control action towards a desired distribution of the agents. The continuum-level control action is then discretized at the agent-level in order to practically implement it. To confirm the effectiveness and the robustness of the proposed approach, we complement theoretical derivations with a series of numerical simulations.

Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarm of drones or robots, or smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly. Also, they often require certain prescribed structural interconnections between the agents (e.g., regular lattices, all-to-all networks etc). In this paper, we provide a distributed displacement-based control law that allows large group of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility. We show the validity and robustness of our approach via numerical simulations and experiments, comparing it with other approaches from the existing literature.