Papers

The Synergetic Extreme Condition User Facility (SECUF) is a comprehensive, state-of-the-art user facility designed to provide integrated extreme physical conditions-including ultrahigh pressure, ultralow temperature, strong magnetic fields, and ultrafast optical fields-for frontier research in condensed matter physics and materials science. Within SECUF, the F2 Sample Pre-selection and Characterization Station plays a pivotal supporting role. Its mission is to provide comprehensive sample synthesis, processing, pre-screening, and characterization services to prepare high-quality specimens for subsequent experiments under extreme conditions. This paper details the specifications and performance of ten core instrument systems within these units. Furthermore, we highlight several breakthrough scientific achievements enabled by the F2 Station, encompassing the discovery of novel quantum spin supersolid states, pressure-induced high-temperature superconductivity in nickelates, giant anomalous Hall angles, and molecular water in lunar soil. We also outline ongoing technical developments that expand the station's capabilities, such as integrated high-pressure cells and self-built ancillary measurement systems.

Rotational irrigation scheduling in water-scarce Central Asia is a densely coupled combinatorial problem: soil-moisture memory links each irrigation decision to all later days within a zone, field adjacency couples zones on overlapping window days, and rigid canal rotations quantize water delivery in time. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) by linearizing the root-zone water balance, so the quadratic crop-stress objective generates the physical couplings as 2-local Ising interactions with no higher-order terms; only the water-budget constraint requires an artificial all-to-all penalty, which we certify with an instance-adaptive weight bound an order of magnitude tighter than generic prescriptions. Every instance is built from observed data for a cotton district in Khorezm, Uzbekistan: NASA POWER meteorology, FAO-56 Penman–Monteith evapotranspiration, SoilGrids~2.0 hydraulics, measured capillary fluxes, and documented canal-rotation windows that enter as qubit-count reductions. We benchmark four tiers – exact solvers, matched-budget heuristics, ideal-statevector quantum approximate optimization algorithm (QAOA), and noise-model plus IBM Heron execution – and add a scaling study on soil instances up to 584 variables. Exact branch-and-bound proves optimality in seconds through 150 variables, and heuristic degradation at fixed evaluation budget is repaired by scaling the budget, so no classical scalability wall appears at deployment-relevant sizes, and none is claimed. On hardware, the informative signal is optimum-sampling enrichment over uniform sampling. We claim no quantum advantage; we deliver a physically grounded, data-complete, reproducible encoding of a societally critical scheduling problem for the quantum-utility era.

A theoretical framework for quantum annealing based on $\mathfrak{su}(3)$ algebra is proposed, and applied to the problem of overcoming first-order transitions in rugged energy landscapes. Conservation of the Casimir invariant means that one can work with irreducible representations of $\mathfrak{su}(3)$, avoiding the exponentially large Hilbert spaces of spin glass systems. In this framework, quantum drivers exhibit nonlocal properties in the sense that during annealing the wave function can be transported far away from a local minimum, thereby avoiding being trapped by it. We consider Hamiltonians with two quantum drivers and studied them numerically. It is shown that energy gap closures can be circumvented via a suitable path in the parameter space of the two drivers. Comparison with more traditional annealing driven by transverse field and antiferromagnetic operators suggests that $\mathfrak{su}(3)$ drivers are more effective in attaining the global minimum of rugged energy landscapes.

Polar nanoregions (PNRs) are central to understanding the exceptional dielectric and piezoelectric properties of relaxor ferroelectrics and are key to advancing dielectrics for high-energy storage. However, direct real-space imaging of their formation and evolution remains a major challenge in condensed matter physics. Here, we report the real-space mappings of both PNRs and chemically ordered regions (CORs) in the prototypical relaxor Pb(Mg1/3Nb2/3)O3 and their temperature dependence using convergent-beam electron diffraction (CBED) combined with four-dimensional scanning transmission electron microscopy (4D-STEM). The results reveal that CORs, with sizes of 2-5 nm, remain static with temperature and act to suppress PNR growth. In contrast, PNRs evolve from isolated 2-5 nm regions at room temperature to interconnected structures ~10 nm in size at low temperatures, indicative of a percolation transition. These observations support the random-field model, in which PNRs emerge from a paraelectric matrix and their growth and collective interactions are constrained by random local fields associated with CORs.

We present a framework to study the orientation distribution of nearby double white dwarf binaries with LISA, explicitly accounting for the fourfold degeneracy in the polarization angle. Exploiting the belt-like sky distribution of the nearby sample along the Galactic plane, we show that the low-multipole extraction can be recast as an effectively one-dimensional Fourier analysis along Galactic longitude, within a spherical-harmonic expansion based on the Wigner-\(D\) formalism. The symmetry with respect to the Galactic plane governs the sensitivity: modes even under reflection across the plane remain accessible, while odd modes are strongly suppressed. For a fiducial sample of about 500 binaries within \(\lesssim 4\) kpc, corresponding to roughly 5% of the resolved Galactic population, the method yields an uncertainty of order 0.02 for the even coefficients with \(\ell \leq 3\), and is readily applicable to the forthcoming LISA catalog.

Due to their vast sizes and extremely low densities, the dynamics of cosmic voids are largely decoupled from complex, small-scale baryonic physics and are highly sensitive to the background expansion of the Universe. This makes them clean and sensitive probes of dark energy's properties. Using N-body simulations, we show that the void radial velocity and velocity dispersion profiles are sensitive to the Type 3 interacting dark energy model parameters, the momentum coupling $\beta$ ($<0$) and the scalar field parameter $\lambda$. Within the $1\sigma$ range of the best-fit values of $\beta$ and $\lambda$ constrained by Planck CMB, DESI BAO, and DES-Y5 supernova data, we find up to $\sim 30\%$ deviations in the void radial-velocity and velocity-dispersion profile spans relative to the uncoupled scenario, which can be well-approximated by a 4-parameter quadratic regression model. This demonstrates that the void radial velocity statistics provide an independent and observationally accessible probe of the dark-sector interaction in the Type 3 model.

A discrete formulation of the multiphase moist compressible Euler equations is presented for which the energy and tracer variance conserving dynamics is coupled to the non-equilibrium moist phase exchanges in a thermodynamically consistent manner. A neural network is then trained to learn the mass exchanges between vapour, liquid and ice phases in atmospheric convection. The network is trained on convection resolving output from a regional configuration of the LFRic model with a multi-moment microphysics parameterisation (CASIM). The loss function for learning these phase exchanges is formulated under the assumptions of thermal and mechanical equilibrium (same temperature and pressure for all phases), and chemical dis-equilibrium (different Gibbs free energies for all phases). The network outputs determine the exchanges of vapour, liquid and ice so as to conserve mass, and the resulting change in entropy is determined from the network outputs so as to conserve energy. The neural network is implemented in a thermodynamically consistent manner within the discontinuous Galerkin moist compressible Euler model in order to simulate the formation of three-phase clouds for convection at sub-km resolution. The results are compared to those from a physics based representation of three-phase moist processes at thermodynamic equilibrium, as well as to an idealised configuration of LFRic model.

R-FLEX is a compact, low-part-count flexure-based radial positioning mechanism designed for the next generation of massively parallel fiber-fed spectroscopic telescope instruments. Current instruments such as the Dark Energy Spectroscopic Instrument (DESI) employ 5,000 robotic fiber positioners at 10.4 mm pitch, whereas future surveys require 2.5-3x higher packing density, necessitating new positioning technologies. The R-FLEX mechanism converts small rotations at the flexure base into large, nearly tilt-free radial motion at the fiber tip, achieving naturally low-backlash linear motion within a compact 5.8 mm diameter package. Coupled with a rotating theta-stage, this enables overlapping circular patrol areas at 6.2 mm pitch, as envisioned for projects like Spec-S5. Development proceeded through parametric modeling and optimization, finite element analysis, fabrication, and prototype testing. Prototype units characterized by optical centroiding achieved a corrected radial accuracy consistently better than 4 um RMS over a 3.9 mm travel range, with maximum fiber tilt 0.092 deg, defocus 42 um over the required range, and durability over more than 400,000 targets per robot across operating and survival temperature extremes. Preliminary Spec-S5 accuracy, defocus, and fiber tilt requirements are specified for the complete R-theta robot, and these radial-stage results exceed those requirements, leaving margin for the companion theta-stage. A parametric optimization pipeline makes R-FLEX a versatile, mass-producible platform that can be re-optimized for precision-positioning applications beyond Spec-S5. This offers the precision, compactness, and reliability needed to collect hundreds of millions of spectra for new studies of the large-scale structure of the universe.

Hybrid quantum-classical performance depends on how quantum processors share classical compute, controllers, memory, and communication links. Circuit simulators and provider measurements expose kernel and job behavior, but leave topology-level questions about controller bottlenecks, quantum capacity, and mixed-workload contention unanswered. We present HybridQC, a discrete-event simulator for configurable hybrid compute units (HCUs). Users specify resource graphs containing classical compute, memory, controllers, quantum annealing (QA) and digital quantum computing (DQC) devices, and links. Jobs become dependent stages for preparation, quantum execution, transfers, reconstruction, and postprocessing. Finite capacities and selectable scheduling policies determine where stages wait and overlap. Service profiles use measurements from D-Wave Advantage 1 and Advantage 2 and IBM Kingston, Marrakesh, and Fez, separating physical quantum-processor occupancy from cloud wall time. Mean absolute percentage errors are 3.92%-8.04% for D-Wave access time and 5.26%-19.01% for IBM quantum seconds. Balanced 10x HCU scaling improves makespan by 2.19x-3.42x, while policy choice changes makespan by 1.80x for a mixed workload with 20 jobs per workload family. Scaling depends on which inputs grow: 100x data growth yields median runtime near 306 s, while 100x joint growth in circuits, shots, and depth reaches 4.806 x 10^7 s on the fixed HCU. HybridQC enables designers to study topology, scheduling, and workload growth before tightly integrated quantum-classical machines exist physically.

Fault-tolerant quantum applications require timely classical decoding and feedback. Component benchmarks leave open how contention across processors, links, and finite buffers affects application progress. We propose TandemQEC, a system-level simulator connecting applications, code-specific quantum error correction schedules, and physical modalities. It models dependencies, resource availability, and buffer occupancy to explain joint provisioning decisions. We validate TandemQEC using analytical and CUDA-Q Logical references and measured pipelines. Across 960 repeated GPU/CPU runs using CUDA-Q Realtime, median absolute relative errors are 1.06% for median latency and 1.74% for 95th-percentile latency. Separate CPU-window validation covers PyMatching and BP-OSD across five code families. Studies span superconducting, trapped-ion, and neutral-atom modalities. At 32 concurrent jobs, increasing decoder capacity and bandwidth by 8x together achieves a 4.17x speedup, while either upgrade alone reduces runtime by at most 2.1%. 4x faster syndrome extraction even raises runtime under fixedduration protection due to generating more classical work. Joint upgrades recover reference performance. These findings show why component improvements require balanced provisioning to benefit applications. TandemQEC identifies useful resource combinations before complete systems are available.

The power of Large Language Models (LLMs) has led us to investigate how they might be fine-tuned for learning the "language of molecular geometry". The fine-tuning of the LLMs using Cartesian coordinates or Z-matrices provides an extremely simple method for accurately predicting equilibrium structures and diverse sets of conformers of small organic and drug-like molecules with excellent accuracy and outperforming specialized deep learning models. While the most common representation of molecular geometries are Cartesian coordinates performs adequately, we find that the inherent invariances and relational nature of geometries represented as Z-matrices provides a better grammar for LLM adaptation. Finally, we show that enhancing an LLMs capabilities for robust prediction of small molecule geometries still retains nearly all of its pre-trained language abilities by randomly mixing in small quantities of natural language prompt-response pairs into the fine-tuning.

We investigate a radially symmetric initial-boundary value problem whose separation of variables leads to a regular self-adjoint Sturm-Liouville problem with explicitly determined coefficients and a positive weight function. The resulting Sturm-Liouville operator is defined by a special geometry-induced coefficient pair that gives rise to a nonstandard weighted spectral structure. The associated radial operator possesses a discrete real spectrum and a complete orthonormal system of eigenfunctions in the corresponding weighted Hilbert space, yielding an exact spectral representation of the evolution problem. An exact transcendental spectral equation governing the eigenvalues is derived. To analyze the high-frequency regime, the Liouville-Green transformation is applied directly to the radial Sturm-Liouville equation. This yields explicit asymptotic formulas for the eigenvalues and eigenfunctions together with corresponding Liouville-Green quasimodes. Their weighted orthogonality properties and asymptotic completeness are established within the spectral framework of the exact operator. The resulting asymptotic spectral data are used to construct approximate solutions of the original boundary-value problem and to obtain error estimates for the spectral reconstruction. Numerical computations of the exact Sturm-Liouville spectrum show excellent agreement with the Liouville-Green predictions, thereby validating the asymptotic quantization formula and confirming the accuracy of the proposed spectral approximation.

Large ion crystals in diverse geometries are a key resource for quantum simulation experiments. In this work, we introduce a macroscopic rf trap that supports a wide variety of one-dimensional ion configurations as well as lateral two-dimensional crystals with more than 100 ions. Our design is based on precision-machined fused silica wafers that are stacked to form the trap structure. Ten independently biased electrodes provide flexible control over the axial potential, enabling long one-dimensional crystals, isospaced ion strings, split-well chains, and two-dimensional arrays with tunable aspect ratios. We present the design and fabrication process for this trap and demonstrate the ability to tune the radial secular frequencies, detect and compensate micromotion, rotate the principal axes, and characterize trapped ion heating rates. All trap design and documentation files are freely available alongside this work, to facilitate adoption and further development within the ion trap community.

We investigate the one-loop renormalization of a Yang–Mills–Faddeev–Popov theory deformed by a complex scalar doublet with explicitly broken $SU(2)$ gauge symmetry. At the classical level, we map the complex doublet into a real $SO(4)$ multiplet and discuss its generalization to $SU(N)$ through $SO(2N)$. The symmetry breaking is introduced by splitting the scalar masses and quartic couplings. Using the background field method and the Schwinger–DeWitt technique, we compute the one-loop divergences and establish renormalizability at this order. We derive the beta functions, recovering the known gauge-invariant limit, and analyze the RG flow. Although the Yang–Mills coupling remains asymptotically free, the rescaled quartic couplings have no real fixed points, and the one-loop flow develops a divergence at a finite energy scale, even in the symmetric limit. The asymmetry between the rescaled scalar self-couplings increases toward the UV and decreases toward the IR, within the region of nonnegative scalar self-couplings.

Generational stereotypes are widespread, but they often rely on anecdotes, and it remains challenging to disentangle true birth-cohort differences from the universal effects of ageing and historical periods. Using a statistical approach that separates effects of age, calendar time, and cohort, we analyzed Big Five personality data (five broad dimensions of personality) from N=773,714 individuals assessed across 30 years. Traits that shape social interaction diverged between generations, whereas a prosocial core comprising morality, discipline, and emotional awareness was stable across cohorts. Generation Z (born between 1995 and 2012) showed lower excitement seeking and gregariousness alongside higher self-consciousness and anxiety. These findings suggest that cohort change is selective rather than global: the dispositions through which people engage with the social world may be adapting to contemporary cultural conditions, while core prosocial tendencies remain stable across generations.

This study presents a data-driven framework for solving nonlinear wave equations, specifically the Gross-Pitaevskii equation (GPE) and the single-component nonlinear Schrodinger equation (NLSE), using Physics-Informed Neural Networks (PINNs). The approach integrates physical constraints directly into the neural network's loss function, enabling efficient training without requiring labelled data. We implement a PINN-based framework for solitons that models a variety of localized wave structures across both equations. Predicted solutions are compared with exact analytical results and show strong agreement with low error. The method effectively captures soliton profiles in both the GPE and NLSE. The accuracy and flexibility of the framework suggest its usefulness for studying nonlinear differential equations relevant to Bose–Einstein condensates and nonlinear optics.

By viewing scattering amplitudes as computer programs, we connect two goals: exposing useful analytic structure and constructing efficient numerical evaluators for collider phenomenology. Equivalent programs can differ sharply in multiplicity scaling, arithmetic complexity, cancellation, and runtime. Amplitude calculation therefore defines a structured search problem over analytic representations, recursive algorithms, colour and helicity organisation, and reuse of intermediate objects. We embed repository-scale coding agents inside an external generate-evaluate-select loop with frozen evaluators and objectives, and study three optimisation targets. A scaling search moves from BCFW recursion to a specialised fixed-k split-helicity transfer algorithm, reaching an 805x geometric-mean speed-up on the scored grid. A structural search reorganises NMHV terms into R-invariant-style cells and glued supercells, reducing inter-term cancellation. Across twenty QCD and electroweak processes, an exact-operation search reduces counted arithmetic by 47.7x and gives a 5.9x post-hoc Python runtime improvement. In matched pure-gluon component tests from n=4 to n=6, the evolved engine's speed advantage over the tested Sherpa-Comix exact-sum call grows from 10x to 277x, while its gap to process-specialised MadGraph5_aMC@NLO Fortran compiled at -O3 narrows from 77.7x to 13.7x. The searches move between mathematical representations and combine recursion, symmetry, basis reduction, dynamic programming, and shared computation into hybrid amplitude programs. They provide initial evidence that parts of amplitude optimisation can be made systematic through self-evolving program search, while leaving native generator integration and end-to-end event throughput as future tests. The generator comparison is an isolated exact matrix-element call, not a modification of MadGraph or Sherpa.

The Feynman path integral for quantum statistical mechanics is reformulated as Gaussian sampling of the neighborhood of each position configuration. The variance and mean are obtained from ring polymer statistics on a lattice, and from the high temperature expansion of the Wigner-Kirkwood commutation function, respectively. The algorithm avoids multiple temperature nodes for each configuration and the need for numerical cancelation in the statistical averages, which are problematic for conventional path integral quantum Monte Carlo. Analytic and simulation results for the second virial coefficient of helium are compared to laboratory measurements.

In this work, we have performed the theoretical estimation of the lowest boundary of an axion mass. We have analyzed the energy spectrum of the electron in a constant homogeneous magnetic field, taking into account its anomalous magnetic moment. We have applied the obtained results on the energy spectrum of the electron in a constant homogeneous magnetic field with allowance for its anomalous magnetic moment to the hydrogen atom electron, which is located in addition to the Coulomb field of the proton, as well as in the magnetic field produced by the magnetic moment of the proton. We have applied the idea of the restriction for the lowest boundary of the axion mass to the triplet-singlet transition between two adjacent levels obtained as a result of the hyperfine structure splitting of the s-levels in the hydrogen atom. We have concluded that the mass of an axion ought to be more than 5.885*10^(-6) eV. We have also obtained the analytical relation for the lowest boundary of the axion-electron coupling constant and numerically estimated its value: the axion-electron coupling constant ought to be more than 1.670*10^(-16).

In this work we reconstruct two subclasses of theories from the Horndeski class, the most general scalar-tensor theories of gravity with second-order equations, according to a variety of late-universe cosmological data: model independent measurements of Hubble rate from Cosmic Chronometers, as well as Hubble rate and distance measurements from Baryonic Acoustic Oscillations. In the modified gravity family considered, the scalar field is non-minimally coupled to gravity leading to time variations in the gravitational constant at cosmological scales. The theories considered are also consistent with constraints on the propagation speed of gravitational waves. The reconstruction is carried out by using the Gaussian Processes bayesian technique either for the Brans-Dicke and the Cubic Galileon subclasses of Horndeski theory. We find that the most likely Gaussian Process reconstruction for the data favors a cosmology from the Brans-Dicke subclass over $\Lambda$CDM in the range $z \in (0.4, 1.7)$, and for the Cubic Galileon subclass we find that the most likely reconstruction indeed favors modifications of the scalar field to gravity with evolving dark energy density in the whole range of redshift observations.

We derive generalized effective Friedmann and Raychaudhuri equations for a bouncing polymer quantized Bianchi-I spacetime. We further prove that the relation numerically derived in the literature between the matter energy density and the anisotropic shear at the bounce holds exactly for any type of matter content, and derive explicit expressions for the constants that appear in that relation.

We present Candia-v2, an open-source software package that generalizes the $x$-space Candia algorithm to next-to-next-to-next-to-leading order (N$^{3}$LO) accuracy in Quantum Chromodynamics (QCD). The code solves the DGLAP evolution equations for unpolarized nucleon parton densities using a highly efficient logarithmic expansion technique that can be systematically extended to all orders in QCD. Candia-v2 supersedes the previous original C implementation of the algorithm with significantly increased efficiency and improved API. The software is publicly available on GitHub under the GPLv3 license.

We present a quantum-inspired tensor-network framework for solving advection-diffusion-reaction (ADR) partial differential equations. Discretized solution fields are encoded as matrix product states (MPS), while differential operators are represented as matrix product operators (MPOs). Time integration is performed entirely in tensor-network form using explicit Euler updates with controlled truncation. The method is evaluated on one- and two-dimensional ADR problems and compared with high-accuracy Runge-Kutta reference solutions. Numerical results show that the proposed representation remains compact, stable, and accurate across a range of dynamical regimes. The solver captures both local solution profiles and global observables while maintaining small bond dimensions throughout the simulation. These results highlight the potential of tensor networks as efficient structure-preserving tools for PDE simulation in multiple spatial dimensions.

Tokamak–stellarator hybrids seek to combine tokamak-like compactness and confinement with stellarator-like externally generated rotational transform and steady-state operation. In this work, we build on the recent tokamak–stellarator hybrid study using planar dipole-field coils (PDCs) [Yu et al., arXiv:2605.03599], in which the fixed-position, programmable coils on an axisymmetric winding surface generate flexible three-dimensional shaping fields. Using single-stage free-boundary optimization of coil currents and plasma-equilibrium parameters, we construct vacuum and finite-$\beta$ configurations. The vacuum cases show controllable external transform and magnetic well. The finite-$\beta$ cases accommodate various density, temperature, and pressure profiles, producing quasi-axisymmetric (QA) equilibria with self-consistent bootstrap current, favorable Mercier stability, and reduced demand for external current drive. Re-optimization enables $\beta$ ramp-up and access to different field-period QA branches with moderate coil-current changes. At large rotational transform, a toroidally omnigenous (TO)-like configuration exhibits more favorable infinite-$n$ ideal-ballooning behavior than a QA reference with matched profiles, even though ballooning stability is not directly optimized for. These results demonstrate that PDCs provide a flexible platform for achieving optimized finite-$\beta$ hybrid configurations.

We present a comprehensive re-analysis of two star-planet systems: TOI-4438, an M3.5V star hosting a mini-Neptune in a 7.4-day orbit, and TOI-442, an M1V star with a 4-day period planet located within the hot Neptune desert. Both systems were originally identified as transiting planet candidates by TESS and subsequently validated through the radial velocity (RV) method. Our work incorporates new TESS transit data and high-resolution spectroscopy from the SPIRou near-infrared spectropolarimeter. We detect a persistent and relatively strong Zeeman signature in TOI-442, while TOI-4438 exhibits weaker and intermittent magnetic activity, and we infer the stellar rotation periods of both stars from the variability of the longitudinal magnetic field. We jointly fit photometry and RV models for each system. For TOI-4438\,b we combine archival CARMENES data with 81 SPIRou observations and five TESS sectors. This yields a refined planetary mass of $M_{\rm p} = 4.11^{+0.40}_{-0.38}\,M_{\oplus}$ and a radius of $R_{\rm p} = 2.40^{+0.09}_{-0.10}\,R_{\oplus}$, consistent with the previous estimate within 1.1$\sigma$ while improving by 53% the precision on the mass and 22% on the radius. For TOI-442\,b, we add 29 SPIRou RV measurements to an extensive archival dataset, significantly extending the temporal baseline. With a new TESS sector, we tighten the mass to $M_{\rm p} = 28.38^{+0.77}_{-0.73}\,M_{\oplus}$ and radius to $R_{\rm p} = 4.25^{+0.10}_{-0.08}\,R_{\oplus}$, which agrees to the previous values within 1.5$\sigma$ and improves the precisions by 46% and 67% respectively. We find no clear signs of additional planets in the available RV data, although we detect a single-transit event in the TOI-4438 light curve. We compare various RV models and find that those accounting for stellar variability-induced signals yield improved constraints on the planetary parameters.

Quantum Processing Units promise speed-ups for selected computational problems, including combinatorial optimisation, but their industrial utility remains an open challenge. We study an industrial variant of the Job-Shop Scheduling Problem using quantum, quantum-inspired, and classical methods across three platforms: IBM Quantum, the D-Wave Quantum Annealer, and the Fujitsu Digital Annealer. By tailoring formulations to hardware-specific constraints, we show that hardware-software co-design is essential for solution quality and scalability. We benchmark all approaches against an exact classical solver and a MILP formulation, evaluating runtime, solution quality, and scalability. Our results indicate that quantum and quantum-inspired optimisation can support industrial solver selection, integration in classical workflows, modelling decisions, and early proof-of-concept development, while suggesting a potential path towards improved approximations for industrial scheduling.

While synchronization has been well-studied in deterministic oscillators, most underlying oscillators are stochastic in both natural and man-made systems. Yet, the effects of intrinsic stochasticity remain poorly understood. Here, we develop a new mechanism for synchronizing circadian KaiC molecules that have topologically protected cycles. We find a phase transition to synchronization that depends only on the single-oscillator coherence, across a range of molecular changes that determine this coherence. Examining both mesoscopic and macroscopic numbers relevant for cellular and in vitro conditions respectively, we find different scaling properties above and below the phase transition. Our results shed light on several existing experiments and further predict that external changes can be offset by compensatory changes that improve the single-oscillator coherence - demonstrating a tunable pathway between stochastic single oscillators and their robust collective rhythms.

The worldline approach to quantum electrodynamics allows one to construct integral representations combining large numbers of Feynman diagrams. Here I review the state-of-the-art of a long-term effort to make this fact useful for actual multiloop calculations, such as of the scalar and spinor QED vacuum polarization functions and the electron anomalous magnetic moment. After a short historical introduction, and a discussion of the non-standard mathematical challenges involved, I focus on a recent calculation of the two-loop vacuum polarisation in scalar QED and some master integral formulas obtained in this context.

We demonstrate that an array of integrated coupled waveguides can be designed to simultaneously perform the spectral filtering and routing of light. The framework that we present here operates in a regime opposite to the shortcut-to-adiabaticity techniques, but we show it nevertheless also only requires smooth, slow, and small, modulations of the array's parameters. Our design strategy rely on the analogy which exists between the temporal evolution of an Hamiltonian and the propagation of light in an array of coupled integrated waveguides, mimicking spatially in photonics phenomena analogue to the electronic transitions. Interestingly, the chromatic dispersion restores in photonics the correspondence between spatial propagation effect and wavelength dependence. Therefore our framework is able to fully reproduces temporal effects such as resonant transitions, making it suitable for filtering applications. As a design involving modulations, it shares similarities with Bragg-Gratings (BGs): but it adds some extra features that make it complementary in terms of applications and functionality. First it adds routing capability with the seamless management of multi-waveguide configuration. And secondly the framework we present requires only wavelength-scale modulations, order of magnitudes slower than for BGs. The effects we present here may not interfere with BGs geometry and functionality, and could even be combined with the latter.

In this talk, I summarize the highlights of our recent and ongoing work on exotic hadrons involving explicitly exotic heavy-flavor mesons. By heavy flavors, I refer in particular to strange and charm quarks. As discussed in this manuscript, several puzzle-like features appearing in recent experimental data can be understood in terms of coupled-channel interactions. The main result is the prediction of an exotic meson carrying both charm and strangeness quantum numbers, whose properties cannot be explained as those of a simple quark-antiquark bound state. I also discuss the future prospects of our research program on this topic.