Papers

Indentation is a practical technique for probing the mechanical properties of soft materials such as elastomers and gels when standard tests are difficult, destructive, or simply cannot be performed in situ. Here, deep indentation of a half space made from an isotropic, incompressible one term Ogden hyperelastic solid is analysed for an indenter in the form of a rigid circular rod of radius R and that is either flat-bottomed or has a hemispherical tip. Numerical simulations reveal that a limit state is achieved when the indent depth D much exceeds the radius R of the rod and the indentation force F scales as F = e̱ta} E D^2, where e̱ta} is a dimensionless coefficient and E is the Young s modulus of the solid. The magnitude of e̱ta} increases with the degree of strain stiffening of the hyperelastic solid but is almost insensitive to the headshape of the rod and to the level of Coulomb friction at the interface with the solid. A high level of strain stiffening of the Ogden solid at finite strain implies that the level of strain at the indenter tip is small up to a large value of D/R, thereby protecting the solid from failure and penetration. Directly ahead of the indenter tip the contours of strain energy density are approximately spheroidal in shape and far ahead the Boussinesq solution is recovered. The limit state of deep indentation is compared to that of cavity expansion; whilst several qualitative features are shared, the two limit states differ in their kinematic fields and in their sensitivity to the degree of strain stiffening in the Ogden solid.

Generative AI and high-throughput DFT pipelines propose millions of inorganic crystal structures, but lack a calibrated reference frame against experimentally realized chemistry. Here we embed 167,500 Inorganic Crystal Structure Database entries in a continuous structural-similarity space, partition it into graph communities, and replay them in time. Experimental discovery shows strong structural memory: 82.9% of new formulas enter pre-existing communities; new-community formation falls from 40.2% (1930s) to 2.6% (2010s). The communities are chemically meaningful, positively identifying nine textbook field-defining renaissances, including cuprates, colossal-magnetoresistance manganites, MAX phases, and Li-ion battery cathodes. Projecting GNoME, MatterGen-public, Materials Project, JARVIS-DFT, and Alexandria-PBE into frozen historical maps yields a cutoff-robust ordering: held-out ICSD > MatterGen > {GNoME ~ MP-theoretical} > JARVIS > Alexandria. Structural departure from experimental basins is not specific to generative AI but general across the tested computed sets. Combining structural proximity with reduced-formula precedent defines a historical synthesizability prior for triaging computed materials.

Wächtershäuser's theory proposes iron-sulfur minerals as key platforms for molecular synthesis and supramolecular organization in prebiotic environments. However, defects have been traditionally considered at the center of such assemblies, thereby underestimating the contributions of regular and pristine interfaces. Here, we combine scanning tunneling microscopy and spectroscopy (STM/STS) with density functional theory (DFT) to investigate the fundamental prebiotic chemistry system of L-Cysteine (L-Cys) on defectless FeS$_2$(100) terraces. To do so, we first achieved atomically ordered, defect-free terraces that act as support of two distinct supramolecular phases of L-Cys: one compact, highly ordered supramolecular network and another less packed, labile supramolecular network. We unveil trimer-based intermolecular interactions to be at the origin of these pattern formations. These results demonstrate that L-Cys self-assemblies can be hosted on flawless FeS$_2$ terraces due to the cooperative interplay between substrate electronic structure and intermolecular interactions, without the participation of dominant defects. Therefore, the autocatalytic activity of pyrite could have triggered the on-surface polymerization process of these non-static self-assembled structures under primordial conditions, thereby endorsing Wächtershäuser's postulates on the origin of life.

A lack of spontaneous internal polarization makes cubic GaN (c-GaN) a well-suited material for emerging micro-LED-based short-range communication, where c-GaN promises increased speed over conventional hexagonal GaN (h-GaN). Although c-GaN is metastable, there are well-established methods for growing it in Si V- or U-grooves; the logical step is to truncate these grooves to wedges or inverted pyramids for small devices. There are limited reports of GaN grown in inverted pyramid templates, and the results are contradictory. To study this process, we perform selective area growth of GaN using organometallic vapor phase epitaxy (OMVPE) on Si inverted pyramidal templates and analyze our samples by cross-sectional TEM. We find that polarity is critical to understanding the growth of c-GaN in this four-fold geometry, in contrast to the growth in long grooves. This effect fits within the broader set of challenges of polar-on-nonpolar heteroepitaxy; the c-GaN inside the four-fold symmetric template has its symmetry reduced by polarity to be two-fold. In typical growth conditions – where the underlying h-GaN polarity is uniform – we find this implies that two h-GaN to c-GaN grain boundaries will have a polarity inversion. We observe two different structures at these inverting boundaries, including a previously unreported inversion domain boundary along the basal plane of the undoped h-GaN. These findings show that for small devices – such as micro-LEDs – the polarity-inverting interfaces must be prevented, for example by suppressing the growth of h-GaN on two facets of the template or by locally controlling the h-GaN polarity.

Interactions among building blocks in physical, chemical, and biological systems follow structured patterns interpretable as a learnable language: just as language models learn which words tend to follow others, one can learn which physical phenomena follow others and under what conditions. We introduce an Interaction Language Model (ILM): a framework that treats interaction as a statistically structured, learnable language for design-to-function reasoning across fields. By learning statistical dependencies and ordering among components, ILMs infer interaction pathways, identify missing steps, and predict the next likely interaction. We demonstrate it through two complementary components, PhenoLink and PhenoSeq, applied to molecular diffusion, a ubiquitous mechanism for energy and particle transport. PhenoLink is a directed graph of interactions among diffusion-based events extracted from publications, where each edge aggregates paragraph-level evidence and carries a transition probability interpreted as an information cost. PhenoSeq complements this with a sequence model that proposes missing mechanistic steps under endpoint constraints. Together they form a generate-then-verify pipeline returning every explanation with a reproducible, per-step, corpus-grounded audit trail. Across four held-out suites of 172 queries, the pipeline matches a zero-shot Claude Opus 4.7 baseline on in-distribution accuracy, yet unlike the baseline, which fabricates a chain for every impossible query, it refuses up to 93.5% of transitions in corpus evidence by construction. ILM thus delivers what zero-shot generation structurally cannot: deterministic refusal of evidence-absent queries and auditable, per-step support. The framework extends to natural and engineered systems where function emerges from sequential and conditional interactions among molecules, cells, devices, or other components.

Topological phases of matter are often realized in crystalline materials. To extend their understanding beyond perfect stoichiometry, we introduce a minimal model of a topological random binary alloy and show that the system realizes an exotic form of impurity-band engineering. We reveal that, in contrast to Wannier charge centers pinned by impurities in conventional semiconductors, doping a proximate quantum anomalous Hall insulator results in dopant-centric chiral current loops. The nature of such current loops is intrinsically tied to the properties of both the host and the dopant. We demonstrate that, even at dilute dopant density, these current loops can form topological domains in an otherwise trivial host and trigger a topological phase transition. On the other hand, doping a topological host having chirality opposite to that of the dopants can unexpectedly stabilize a metallic phase in which bulk transport is mediated by inter-domain edge modes.

Photoelasticity is a qualitative and quantitative optical technique to image internal stress distributions in transparent materials. In the past few decades, discrete photoelastic particles have been used as a proxy for dry granular materials in both static, quasistatic, and dynamic analogue experiments. The technique allows the visualization of force chains, determination of the location and magnitude of contact forces, and outputs a stress tensor for each particle with shear and normal stress components. To date, little to no work has investigated photoelastic suspensions, where photoelastic granular particles are immersed in a fluid medium, despite its relevance in industrial and natural applications. The introduction of a fluid phase yields additional considerations in the rheological and photoelastic behavior of our proxy particles. In this manuscript, we summarize the state-of-the-art in resolving forces in immersed photoelastic granular materials. We introduce characterization techniques to probe changes in rheological and optical properties of hydrated photoelastic particles, and we report considerations for use of photoelastic particles in immersion-based experiments. We intend for this work to provide the leading framework to study the hydrodynamic interactions in 2D systems of photoelastic particles immersed in a fluid medium.

Structure and chemical composition of non-stoichiometric $\mathrm{(N \times H)}$-polytypes, $\mathrm{N} \in \{3,\,4,\,5,\,6,\,7\}$, belonging to the family of La2WO6-related tungstates, are presented and the basic rules behind their construction are formulated. The polytypes are validated from the point of view of the internal energy per atom against all the competing La-O-W compounds by means of the ternary convex hull computed by DFT. According to DFT, the ground state of the La2WO6 family surprisingly turned out to be the as-yet hypothetical tungstate La2WO6.oP36, isostructural with the known compound Gd2WO6, closely related to the basic building H-block of the polytypes. One more candidate for an experimentally accessible low-energy structure, La2WO6.mC72, derived from the already observed Sm2MoO6, has been identified to lie only $\Delta E \approx 5\:\mathrm{meV/at.} \cong 60\:\mathrm{K}$ above the convex envelope.

Doping of transition metal oxides such as CaFe$_3$O$_5$ offers a controlled way to tune the interplay of charge, spin, and lattice degrees of freedom, yet local-probe studies remain difficult because strong correlations and dynamic charge-spin fluctuations obscure fine spectroscopic features in powder samples. Here, we employ quantum magnetometry based on nitrogen-vacancy (NV) centers in nanodiamonds impressed into an Mn-doped CaFe$_3$O$_5$ powder pellet to probe static and dynamic magnetic fields at the nanoscale across the weak ferromagnetic transition. The splitting and broadening of the optically detected magnetic resonance (ODMR) spectra exhibit an order-parameter-like increase by ~ 15 MHz upon cooling below the critical temperature, T$_{\rm c}$. Concomitantly, the spin-lattice relaxation rate, 1/T$_1$, exhibits a pronounced, divergence-like enhancement at T$_{\rm c}$, increasing by about one order of magnitude from its high-temperature value. Moreover, detailed lineshape fits of ODMR spectra together with the stretched-exponential NV magnetization recovery curves corroborate the proposed electronic phase segregation in charge-ordered and charge-averaged phases at the nanometric scales. The presented study demonstrates the viability of using nanodiamonds as a platform for nanoscale magnetic probing of strongly correlated matter, including phenomena such as electronic phase separation.

State-of-the-art artificial intelligence (AI) and Machine-Learning (ML) tools have not yet enabled rapid design of next-generation materials. Detailed physical understanding of how material properties affect device performance is required to advance materials development. For example, optimization of ink parameters for electrocatalysts has no known physical mathematical model and thus insights are difficult to translate from material studies to device studies. Herein, we demonstrate how to use the emerging AI tool, physics-based structural causal models (SCMs), to extract quantitative causative insights from complex heterogeneous electrochemical systems with small (n < 10), but multi-modal datasets (modes > 10). Our SCM quantitatively separates the role that varying the support-to-catalyst ratios and total material loadings plays on catalytic performance. The proof of concept model developed in this work enables root-cause-analysis on the cyclic voltammograms of manganese-antimony oxide oxygen reduction electrocatalysts on Vulcan carbon supports tested in alkaline media using a rotating disc electrode device configuration. Our preliminary causal analyses quantitatively disentangle how the catalyst performance is affected by the number of active sites versus the thickness of the electrode. To the best of our knowledge, this is the first demonstration of physics-based SCMs applied to electrochemical materials and their performance.

The COSMO-SAC-Phi model developed by Soares et al. extends the COSMO-SAC activity-coefficient framework into a full equation of state by explicitly accounting for pressure effects. In this approach, pure substances and mixtures are represented as pseudo-mixtures consisting of the actual number of moles and an additional pseudo-component that describes free volume, or holes. In this work, we implement this extension within the openCOSMO-RS framework and evaluate it using a large and diverse set of molecules and binary systems. The resulting equation of state includes an extensive open-source parameter set with around 1800 pure-component entries, made freely available to the academic community. The four pure-component parameters were fitted to vapor-pressure and liquid-molar volume data for each substance. Model performance was assessed against two benchmark equation-of-state databases, one for pure compounds and one for binary mixtures, without introducing any binary interaction parameters. The resulting openCOSMO-RS-Phi model reproduces the accuracy of the original COSMO-SAC-Phi formulation while providing a fully open-source and accessible implementation for the scientific community. Beyond its immediate utility, it also establishes a foundation for future development of predictive EoS for electrolyte solutions.

Over the past decade, semiconductor spin qubits have progressed from few-qubit demonstrations towards larger-scale devices fabricated in increasingly reproducible academic and industrial processes. This progress marks an inflection point: the central challenge is no longer to demonstrate high-fidelity operation in carefully tuned devices, but to discover, verify, and maintain stable operating conditions reliably across many interdependent controls, varied device geometries, and disparate material platforms. In this Perspective, we frame spin-qubit operation as a modular automation problem. We decompose the workflow into five modules: bootstrapping from minimal prior information, configuration tuning, virtualization of physical gates into effective control axes, qubit-level tuning, and an operation layer with drift-aware maintenance. Using recent demonstrations from our work and the broader community, we argue that scalability will depend on explicit interfaces between modules, standardized intermediate data products, and workflow-level metrics such as throughput, success probability, stability time, recovery time, and robustness. We close by outlining the infrastructure needed to move beyond isolated tuning demonstrations toward sustained operation: qubit-performance-aware feedback, reusable software and benchmark tasks, and tight collaboration among experimental, theoretical, and software efforts.

We investigate the low-energy excitation spectrum of the van der Waals heterostructure superconductor 4Hb-TaS$_2$ using ultra-low-temperature specific-heat and thermal-conductivity measurements with magnetic fields applied parallel and perpendicular to the crystallographic $c$ axis. The specific heat is broadly consistent with a nodeless superconducting gap, but retains a finite residual linear contribution, indicating a small residual low-energy density of states in the superconducting state. In addition, a pronounced upturn appears below approximately 0.3K. Its weak magnetic-field dependence, together with the absence of a corresponding feature in thermal transport, supports an interpretation in terms of localized degrees of freedom, most likely a nuclear Schottky contribution. In contrast to the finite residual thermodynamic density of states, the thermal conductivity extrapolates to a vanishing zero-field electronic linear term within experimental uncertainty for both field orientations. Thus, the residual low-energy states do not form a detectable itinerant heat-conduction channel. In finite magnetic field, the electronic heat transport grows rapidly. For out-of-plane fields, this response is broadly consistent with previous thermal-conductivity measurements and with the behavior commonly associated with multigap nodeless superconductivity. The even steeper increase observed for in-plane fields suggests that the field-induced quasiparticle response of 4Hb-TaS$_2$ is more complicated than the standard multigap picture alone.

The prediction of crystal structures is a key challenge in chemistry and materials science, but evolutionary crystal structure prediction (CSP) remains computationally expensive because it relies on repeated ab initio relaxations and energy ranking. Machine learning interatomic potentials (MLIPs) can accelerate CSP, yet their use is limited by the need for large training sets and by the difficulty of choosing which candidate structures should be labeled by density functional theory (DFT). Here we introduce a self-consistent, foundation-model-assisted CSP workflow that combines evolutionary search with adaptive data selection and fine-tuning. Starting from a pretrained MLIP, the algorithm rapidly explores configuration space while iteratively selecting compact, representative, and physically relevant subsets of structures for DFT labeling, thereby reducing redundant calculations and improving a system-specific potential. We apply the method to the chemically complex Ca–Fe–Ni ternary system. The workflow reproduces the known low-pressure convex hull and enables efficient high-pressure exploration. It predicts a previously unreported compound, Ca$_6$FeNi, which becomes thermodynamically stable above 100~GPa. These results show that foundation-model-based, data-efficient CSP can greatly reduce computational cost while preserving accuracy and enabling the discovery of new materials in complex multicomponent systems.

Scanning transmission electron microscopy (STEM) is a cornerstone of our understanding of oxide interfaces and superlattices. No other technique provides the same level of insight into structure, chemistry, composition, and dynamics across as wide a variety of material systems. STEM imaging and diffraction, coupled with electron energy loss (EELS) and energy-dispersive X-ray (EDS) spectroscopies, offer unparalleled, high-resolution analysis of structure–property relationships. In this chapter we highlight investigations into key phenomena, including interfacial conductivity in oxide superlattices, charge screening effects in magnetoelectric heterostructures, interface engineering in iron oxides, and the complex physics governing atomic-scale chemical mapping. We also discuss emerging plasma preparation techniques and artificial intelligence-guided approaches to both ex situ and in situ microscopy. These studies illustrate how unique insights from STEM characterization can be integrated with other techniques and theory calculations to develop more predictive models for the behavior of functional oxides.

We investigate the formation of a barrier to evaporation that develops when levitated nanoscale Au nanoparticles are exposed to pulses of 532 nm laser radiation in a high vacuum (pressure $p=10^{-8}-10^{-7}$ Torr) environment. Our data are derived from precision measurements of the charge to mass ratio ($Q/M$) of $\sim$200 nm diameter Au particles confined in a quadrupole ion trap. We characterize the development of the barrier over time as the particle is repeatedly heated with laser pulses and determine the impact of variations of the interval between pulses and of exposure to several gases added to the vacuum chamber. We observe a slow increase in the mass of particles upon prolonged exposure to the vacuum, which we attribute to the growth of a barrier layer. For particles that have acquired a barrier during exposure to CO, we observe a rapid decrease in their mass upon subsequent exposure to O$_2$. These findings are consistent with the growth and subsequent oxidation of a graphene layer on the Au that forms the barrier to evaporation. However, we have not found that the rate of formation of the barrier depends on the pressure of carbon-containing gases (CO, C$_2$H$_4$, CO$_2$) we have added to the chamber. We hypothesize that a rare surface state on the solid Au particle catalyzes the reaction that introduces C to the particle. Repeated laser pulse heating is necessary–either to enable diffusion away from this state or to create fresh states that allow continued C uptake–to facilitate the growth of the surface graphene layer.

Magnonic systems, which exploit spin-wave excitations in magnetic materials, offer a promising platform for coherent information processing due to their low dissipation, strong nonlinearities, and intrinsic nonreciprocity. However, scaling magnonic circuits remains challenging, particularly with low-loss insulators such as yttrium iron garnet (YIG), which are difficult to pattern. Here, we experimentally realize a synthetic dimension in a magnonic system by coupling multimode magnon resonances in the frequency domain using time-periodic Floquet modulation. This approach enables electronically tunable interactions between discrete modes within a single YIG device, forming a reconfigurable mode-space lattice that supports functionalities such as Bloch oscillation. Our results demonstrate that high-dimensional magnonic dynamics can be achieved without increasing device footprint, establishing synthetic dimensions as a scalable and programmable route for integrated magnonic technologies. This advancement positions magnonic systems as promising platforms for engineering emergent phenomena that are inaccessible at equilibrium.

We derive the exact image-dipole potential of a point dipole inside a closed cuboidal superconducting trap. The construction generalises the parallel-plate result to a geometry that confines every translational degree of freedom, and we prove that the image lattice satisfies the Meissner boundary condition on all six walls. For a centred dipole the orientational energy reduces to a diagonal quadratic form whose three coefficients are Epstein-zeta-type lattice sums. We show that in both the infinite and finite rectangular traps the dipole orientation aligns with the short cross-sectional axis over a finite range of aspect ratios. The equilibrium orientation in both cases is described by a phase diagram whose degeneracies we classify. Every prediction is verified against finite-element solutions of the same boundary-value problem, with agreement better than $0.16\%$.

Transport properties of (quasi)particles in condensed matter depend profoundly on the spatial dimension. Motivated by recent advances in growing ultrathin magnetic films and monolayer van der Waals magnets, we present a theory of magnon transport in magnetic films spanning the crossover from bulk to the two-dimensional (2D) limit. We find a magnon conductivity that diverges~logarithmically in magnetically soft but stable (quasi)2D magnets with long-range dipolar interactions. This critical enhancement is absent in bulk systems and may explain the unusually large magnon conductivities recently observed in ultrathin yttrium iron garnet films. Our results reveal an intrinsic mechanism for enhanced magnon transport in low dimensions and highlight the potential for engineering high-efficiency magnon conductors in atomically thin magnets.

We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach to the XY model, a fundamental continuous-spin model in condensed matter physics, we show that our technique addresses the shortfalls of the Markov chain Monte Carlo (MCMC) in generalization to varying system sizes. More specifically, we show that training a temperature-conditioned diffusion model on smaller-size XY model lattices enables the generation of accurate samples in larger lattice sizes. By tracking physically important observables of the model, such as spin correlations, our experiments demonstrate that diffusion sampling followed by a few MCMC steps reduces the thermalization time by an order of magnitude relative to the standard MCMC with random initialization. Our study provides valuable insight as to how generative models can be used to study continuous-state condensed matter systems at scale.

We investigate the interplay between cavity-induced density-wave ordering and controllable disorder in a trapped two-dimensional gas of ultracold fermions. The atoms are dispersively coupled to an optical cavity and transversely driven by a pump beam, while an additional speckle beam spatially modulates the atom-light coupling through an AC-Stark shift of the atomic transition. In momentum space, this disorder converts the usual coupling between the cavity mode and a discrete set of density-wave Fourier components into a coupling to a continuum of fermionic density modes, weighted by the spectrum of the speckle pattern. Using linear response theory, we derive the superradiant threshold and show that the disordered interaction renormalizes the effective light-matter coupling, lowering the critical pump strength on average, with the threshold becoming self-averaging for short speckle correlation lengths. We complement this analysis with a numerical mean-field treatment that gives access to the intracavity photon number and to the real-space fermion density across the transition. These results confirm that the disorder shifts the photonic phase boundary and, above threshold, distorts the density-wave crystal by populating Fourier components beyond those selected by the clean cavity geometry. Our findings identify both the emitted cavity light and in situ density images as probes of engineered disorder in fermionic matter coupled to optical cavities.

Motivated by the recent discovery of superconductivity at 16.3 K in layered oxychalcogenide Na$_{2-x}$V$_2$Se$_2$O we investigate pairing instabilities in the broader family of layered materials composed of V$_2$O planes, believed to exhibit altermagnetic order in their monolayer form. Even though the bulk family members KV$_2$Se$_2$O and Rb$_{1-\delta}$V$_{2}$Te$_{2}$O are likely conventional antiferromagnets that show only surface altermagnetism, our analysis predicts exotic equal-spin triplet superconductivity as the dominant pairing instability in these materials. This is a consequence of their unique magnetic and sublattice structure that renders electron bands incompatible with conventional spin-singlet pairing. The predicted triplet superconducting phases are topologically non-trivial and capable of supporting spin-polarized persistent currents, properties potentially useful in technological applications.

Integrable quantum systems evolving from non-equilibrium initial states do not thermalize to conventional Gibbs ensembles (GE). Instead, at long times they relax to generalized Gibbs ensembles (GGEs), which incorporate the full set of local and quasi-local conserved quantities. While GGEs have been extensively studied in the literature, a quantitative analytic characterization of how different they are from ordinary GEs is still lacking. In this work, we address this question by employing the concept of athermality, which we define within quantum resource theory as the relative entropy between a given state and the closest thermal state. By means of integrability techniques we compute the athermality for several quantum quenches in paradigmatic integrable models, including the free XY spin chain, the interacting Lieb-Liniger model, the XXZ spin chain, and the harmonic chain. We find that often the athermality becomes anomalously small when the post-quench Hamiltonian is critical in its ground state, despite probing physics at a finite energy density. We also prove that it systematically develops a singularity at criticality, which is inherited from the entropy of the GGE.

Collective light-matter phenomena such as Dicke superradiance are often described as a collection of effective spins coupled homogeneously to a bosonic mode, giving rise to a collective bright mode with enhanced light-matter coupling. In fermionic systems, Pauli exclusion and Fermi-surface structure can significantly modify this picture, while randomness in the atom-light couplings raises the question of whether disorder promotes or suppresses collective behavior. Here, we study a cavity model in which fermionic particles couple to a photonic mode through a random all-to-all interaction matrix with tunable mean and variance. Combining numerical mean-field methods, analytic stability analysis and random-matrix predictions, and benchmarks against exact diagonalization, we characterize both the onset and structure of the superradiant phase. While mean coupling and disorder variance contribute in the same way to the onset, they lead to drastically different behavior within the condensed phase. Uniform coupling supports a single bright collective fermionic mode with conventional Dicke-like scaling of the cavity field. Disorder, instead, gives rise to a qualitatively different collective regime in which many grey fermionic states participate coherently, producing a parametrically enhanced scaling of the condensate with system size. Our results reveal a mechanism through which disorder can, perhaps counterintuitively, promote collective light-matter phenomena.

Bloch oscillations (BOs) are a quantum phenomenon in which electrons subjected to an electric field in a periodic potential exhibit an oscillating current without a net drift. In real conductors, scattering reduces the coherence required for BOs driving the system toward a steady state with a DC current. While previous studies have focused on charge transport, charge carriers also possess spin, raising the question of whether BOs can emerge in magnetic observables. Here, we show that the magnetization of odd-wave magnets can undergo BOs before relaxing to the steady-state Edelstein value, a phenomenon we term $\textit{magnetic}$ BOs. Using analytical and numerical methods, we demonstrate this effect in a minimal one-dimensional model of a p-wave magnet and generalize it to two dimensions. Our analysis further reveals that the Edelstein magnetization is generically nonlinear in the applied electric field. Finally, we argue that magnetic BOs can be detected in materials through higher-harmonic generation in THz sub-cycle lightwave spectroscopy. Magnetic BOs provide a genuine non-equilibrium signature of spin-charge coupling in unconventional magnets.

We analyze the exact dynamics of a generalized quantum Brownian motion model, employing Gaussian master equation methods. We demonstrate that, by modulating the relative weights of specific interaction channels, we can control the degree of non-Markovianity of the system, and induce a transition from non-Markovian to Markovian regimes. The non-Markovianity of the evolution is formally characterized by leveraging the Gorini–Kossakowski–Sudarshan–Lindblad theorem and by employing quantitative measures of information backflow. Finally, we clarify the physical mechanism behind the phenomenology of this model, thereby providing a systematic platform for environment engineering through the strategic tuning of dissipation channels.

Complex conformal field theories (CFTs) have recently emerged as essential frameworks for understanding non-Hermitian criticality, weakly first-order phase transitions, and walking renormalization group flows, while their general structures remain largely unknown. In this work, we propose a systematic construction of complex CFTs by analytically continuing exactly marginal couplings into the complex plane. This procedure applies uniformly to bulk, boundary, and defect deformations, preserving conformal symmetry while generically complexifying operator spectra and other universal data. Using the compact free boson as a solvable laboratory, we uncover the global structure of the complexified Gaussian conformal manifold. More generally, we demonstrate that genuinely complex rational CFTs do not exist: rational points remain confined to the real regime, providing a sharp distinction between real and complex theories. In the defect case, we investigate the one-parameter family of conformal defects in the Ising CFT and derive exact expressions for the defect spectrum, energy transmission coefficient, and effective central charge from analytic continuation. The theoretical predictions are precisely verified in non-Hermitian critical Ising and free fermion chains using bulk-defect correlators, entanglement entropy, and complex energy transport, providing concrete evidence for the complex defect conformal manifold. Finally, we study complex boundary renormalization-group flows through the AdS/BCFT correspondence. Our results establish complex conformal manifolds as a controlled bridge between solvable lattice models, complex CFTs, and holography, while providing stringent analytic benchmarks for the nonunitary conformal bootstrap.

Quantum chaos is commonly associated with the emergence of random-matrix statistics in the spectra of quantum systems. A useful diagnostic is provided by the spectral form factor (SFF), which for random matrix ensembles displays a universal linear-growth regime (`ramp'). In the last decade, a landmark result by Bertini, Kos and Prosen (BKP) identified for the first time a class of geometrically local quantum dynamics of finite-dimensional particles where the SFF provably exhibits a random-matrix ramp: periodically driven (Floquet) qudit chains whose evolution is described by `dual-unitary' circuits. Here, building on the BKP result and on a recently proposed variant of the Feynman-Kitaev clock construction, we obtain a spectral ramp in a class of static, geometrically local Hamiltonians. Our strategy is to embed the Floquet quasienergy spectrum of a dual-unitary circuit into the energy spectrum of a static local Hamiltonian, and to prove that the latter's connected SFF inherits the BKP ramp within a symmetry sector. This is to our knowledge the first proof of a spectral ramp in a time-independent, geometrically local many-body system with finite local Hilbert space dimension.

Non-Clifford gates are a necessary resource for universal quantum computation, yet their fault-tolerant implementation typically relies on magic-state distillation, which incurs significant overhead in qubit count and circuit depth. In this work, we propose a probabilistic cavity-based magic-state injection protocol. Our scheme exploits controlled atom-cavity interactions and conditional measurements to probabilistically prepare an effective magic state encoded in the first two level Fock subspace of a single cavity mode, achieving a success probability of $0.74$ per attempt, independent of the target magic phase. The cavity-encoded magic state is subsequently injected into a computational atom via a teleportation-based protocol mediated by dressed-state transitions, requiring only Clifford operations and a single auxiliary atom for readout. We show that all required operations – state preparation, two-qubit exchange gates, and projective measurement – can be implemented with experimentally available techniques in Rydberg atom-cavity platforms. We further discuss how the scheme can in principle be adapted to operate at the logical level, where collective Rydberg interactions and optical nonlinearities provide a route toward cavity-mediated $T$-gate injection directly into code-encoded qubits.

We show that the effective dynamics of the elitist $(1+M)$ genetic algorithm is, in the limit of small mutations, normalized gradient descent on the loss in the presence of anisotropic Gaussian white noise. In expectation, therefore, a simple mutation-selection genetic algorithm follows the gradient of the loss, without explicit calculation of gradients and without averaging over loss evaluations. The genetic algorithm is slower than gradient descent because of the noise that acts in directions transverse to the gradient. However, this slowdown is controlled not by the number of parameters of the search space but by the effective rank of the Hessian of the loss function. For the concentrated Hessian spectra observed in neural-network loss functions the effective rank can be far smaller than the number of parameters, which may explain why genetic algorithms can scale to large search spaces.