Knotted fields in classical and quantum systems have long been recognized for their nontrivial topologies and particlelike behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures—heliknotons—were discovered in chiral liquid crystals, forming adaptive crystal assemblies via elastic distortion-mediated interactions. We use machine learning to develop single-site coarse-grained potentials that accurately capture these chiral anisotropic effective interactions. The resulting potentials accurately reproduce experimentally observed heliknoton assemblies and enable simulations at length scales and timescales far beyond the range of fine-grained continuum models. This general framework is readily transferable to other topological solitons, providing a powerful route to understand, predict, and ultimately control their collective behavior and dynamics.

Highly motile cells, like patrolling leukocytes during immune response and amoeboid sarcoma cells during metastasis, navigate relatively long distances in complex environments where they encounter a wide variety of topographic structures on different length scales. Subcellular topographic substrate anisotropies have been shown to cause a drift in cell migration (topotaxis) in vitro, indicating that the topography of the extracellular environment can function as a physical guidance cue. Here, we expand on these previously reported subcellular findings by showing that supercellular asymmetries spanning long distances can also induce topotactic drift. Such long-range topotactic drift was generated by exposing highly motile cells to a subtle spatial gradient in the arrangement of cell-sized pillars. Using an analytical model and mean first-passage time analyses, we show that this type of long-range topotaxis emerges through the many interactions of persistently random migrating cells with the anisotropic confinement imposed by the gradient pillar field. We demonstrate that pillar-induced long-range topotaxis is conserved during chemotaxis, the cellular drift in response to chemical gradients. Topotactic and chemotactic drifts were found to add linearly, a finding that could have implications for in vivo studies on directed cell migration, which mostly focus on chemotactic responses alone. Moreover, as long-range topotaxis is an independent physical effect, it offers a way to steer highly motile cells independently from chemotactic guidance, which could have potential applications in cell sorting and tissue engineering.

The efficiency of adiabatic quantum evolution is governed by the evolution time $T$, which typically scales as $\mathcal{O}\left({\mathrm{\Delta }}^{-2}\right)$ with the minimum energy gap $\mathrm{\Delta }$. However, the rigorous lower bound is $\mathcal{O}\left(L{\mathrm{\Delta }}^{-1}\right)$, where $L$ is the adiabatic path length. Although $L$ is formally upper bounded by $\mathcal{O}\left({\mathrm{\Delta }}^{-1}\right)$, such a bound is often too loose in practice, and $L$ can be bounded independently of $\mathrm{\Delta }$. This indicates the potential for a quadratic speedup through adiabatic schedule construction. Here, we introduce the constant geometric speed (CGS) schedule, which traverses the adiabatic path at a uniform rate. We show that this approach reduces the scaling of the evolution time by a factor of ${\mathrm{\Delta }}^{-1}$, provided $L$ remains bounded independently of $\mathrm{\Delta }$. We propose a segmented CGS protocol where path segment lengths are computed from eigenstate overlaps on the fly, reducing the prior spectral-knowledge requirement from the full gap function $\mathrm{\Delta }\left(s\right)$ to just a global lower bound on the energy gap. Numerical tests on adiabatic unstructured search, ${\mathrm{N}}_2$, and a [2Fe-2S] cluster demonstrate the optimal ${\mathrm{\Delta }}^{-1}$ scaling, confirming a quadratic speedup over the standard linear schedule.

We study a vertical double quantum dot (DQD) in a $\mathrm{Si}/{\mathrm{Si}}_{1-x}{\mathrm{Ge}}_x/\mathrm{Si}$ double-well heterostructure for full electrical control of electron Loss-DiVincenzo (LD) spin qubits, using realistic device modeling and numerical simulations. Due to the emerging spin-orbit interaction in the DQD, as well as strain from the gate electrodes, small (percentage range) but finite $g$-tensor variations emerge. In addition, we find a large valley splitting, on the order of $E_v\sim 250\mu \mathrm{eV}$. As a result, multiple avenues for fast electrical single-qubit rotations emerge. An ac electric field gives rise to electric dipole spin resonance, while electron spin resonance in the presence of an ac magnetic field can be electrically controlled by local gates due to varying $g$ factors in DQDs. We also show that shuttling between neighboring dots, in vertical and horizontal directions, results in ultrafast single-qubit gates of less than a nanosecond. Remarkably, this DQD architecture completely eliminates the need for micromagnets, significantly facilitating the scalability of LD spin qubits in semiconductor foundries.

Energy-transfer efficiency is an important quantity in plasma-wakefield acceleration, especially for applications that demand high average power. Conventionally, the efficiency is measured using an electron spectrometer, an invasive method that provides an energy-transfer efficiency averaged over the full length of the plasma accelerator. Here, we experimentally demonstrate the use of the excess light emitted by the plasma after a beam-plasma interaction in a method that yields noninvasive, longitudinally resolved measurements of the local energy-transfer efficiency from the wake to the accelerated bunch. As we show, by diagnosing the acceleration process directly at the plasma location, this method can provide more insight into effects such as trailing bunch charge loss, which typically impose systematic errors on downstream spectrometer measurements. In the future, this method would also be suitable for the online optimization of individual stages in multistage plasma accelerators.

The coherence characteristics of a tin-vacancy (SnV) color center in diamond are investigated through optical means, including linewidth broadening effects and coherent population trapping (CPT) between the ground-state orbital levels. Spectral analysis is required as due to the large spin-orbit splitting of the orbital ground states, thermalization between the ground states occurs at rates that are impractical to measure directly in the time domain. First, by implementing a temperature-dependent linewidth broadening measurement, including the challenging-to-measure D transition, phononic coupling coefficients are determined. These measurements are performed on an emitter with a lifetime-limited linewidth and atomlike properties, making the measurement representative for high-quality SnVs. Next, a CPT-type experiment is carried out to independently analyze thermal decoherence processes at 4 K. The spectral information is transformed into its conjugate variable time, providing picosecond resolution and revealing an orbital depolarization timescale of $\sim 30\mathrm{ps}$. Consequences of the investigated dynamics are then used to estimate spin dephasing times limited by thermal effects.

We propose an experimental protocol to measure generalized temporal entropies in many-body quantum systems. Our approach involves using local operators as probes to characterize the out-of-equilibrium dynamics induced by a geometric double quench on a replicated system. Such protocol mimics the path integral on the corresponding Riemann surface encoding generalized temporal entanglement. We present the results of tensor network simulations of one-dimensional systems that validate the protocol and demonstrate the experimental feasibility of measuring generalized temporal entropies, and we outline the experimental requirements for implementing these quenches using state-of-the-art quantum simulators. Therefore, our results provide a physical interpretation of the meaning of generalized temporal entropies. Furthermore, they reveal that the dynamics induced on two replicas of the Ising model in a transverse field differ qualitatively from those of its nonintegrable extension, suggesting that generalized temporal entropies can be used as a tool for identifying different dynamical classes in quantum systems.

The impact of disorder on wave transport has been extensively studied in Hermitian systems, where static randomness gives rise to Anderson localization. In non-Hermitian lattices, static disorder can lead to peculiar transport features, including jumpy wave evolution. In contrast, much less is known about how transport is modified when the on-site disorder evolves during propagation. Here, we address this problem by investigating two pertinent non-Hermitian lattice models with disorder altered at regular intervals, characterized by a finite disorder period. In lattices with symmetric couplings and complex on-site disorder, short disorder periods suppress localization and give rise to diffusionlike spreading, while longer periods allow the emergence of jumps. In Hatano-Nelson lattices with real on-site disorder, the non-Hermitian skin effect asymptotically dominates regardless of the disorder strength, while the disorder period reshapes the drift velocity and modulates its competition with Anderson localization. These results establish evolving disorder as a way of tuning non-Hermitian transport.

We study thermal transport along the edge of a small chiral spin liquid device coupled to two Ising-chain reservoirs, a platform suitable for quantum-engineered systems. Adiabatically switching on the tunnel couplings to the reservoirs generates a thermal current that dynamically builds up and reaches a quasi-steady-state regime. In this experimentally critical time window, the two-terminal thermal conductance can approach half-quantized values—a hallmark of Majorana-mediated transport—under finely tuned conditions. The results agree with an effective Landauer-Büttiker description in the large-reservoir limit, where energy-resolved transmission rates help identify the optimal parameters to achieve the half-quantized conductance. This work provides a controllable platform to investigate topological thermal transport in engineered spin systems, such as realized in cold-atom and Rydberg-atom settings.

The interplay of twist and strain in bilayer graphene enables the formation of moiré patterns and narrow bands that host correlated and topological phases. While magic-angle twisted bilayer graphene has been widely studied, strain provides an additional and realistic control knob for band engineering. In this work, we first generate a global method to construct commensurate supercells for arbitrary twist and heterostrain. Then, using atomistic tight-binding and strain-extended continuum models to study the commensurate structures, we identify configurations that minimize the bandwidth beyond the magic angle. The results reveal a strong dependence of band narrowing and topology on strain type, magnitude, direction, and lattice relaxation. Particularly, shear strain produces a stronger distortion than uniaxial strain. Including electron-electron interactions through a self-consistent Hartree potential shows that strain broadens the bare bands while reducing electrostatic renormalization. Strain also drives topological transitions as the narrow and remote bands hybridize, establishing twisted and strained bilayer graphene as a tunable platform for flat-band and topological phenomena.

Physics-informed machine learning has proven effective for learning conservative dynamical systems. However, most real-world systems are nonconservative, and current approaches typically handle dissipation through ad hoc modifications or require access to full-state information. Here, we introduce the discrete Herglotz Lagrangian neural network (HLNN), a structure-preserving framework for learning a class of smooth dissipative dynamics from data. Grounded in the Herglotz variational principle, HLNN extends classical variational learning to dissipative systems by modeling dissipation intrinsically within a contact-geometric framework, without prescribing an explicit empirical dissipation law, and can be trained from position-only observations without explicit velocity supervision. Across benchmarks spanning state-dependent damping, time-modulated damping, and external driving, HLNN reliably recovers long-term trajectories and dissipation rates, outperforming baseline models on the considered benchmarks. Our results establish a unified variational and geometric framework for learning smooth dissipative dynamics and open avenues for data-driven modeling of nonequilibrium and open systems.

In passive phase-separating mixtures, the average density of each component can be tuned to control the composition of the coexisting bulk phases. This concept extends to active systems. For example, in a nonreciprocal mixture of two species, changing the average density of either species alters the qualitative nature of the traveling patterns that emerge in the steady state. In this paper, we build on the existing framework of the multispecies nonreciprocal Cahn-Hilliard model to examine the influence of compositional disorder in a multicomponent active system. Specifically, we allow each scalar field in the mixture to have a distinct average density and analyze the implications of this generalized setting. Focusing on ensembles of systems in which the interspecies interaction coefficients and the average densities of each species are both sampled from probability distributions, we show that nonreciprocity stabilizes the uniformly mixed state, even in the presence of compositional disorder, when compared to the corresponding reciprocal system. Using random matrix theory, a general condition for the onset of the spinodal instability is derived and verified through simulations. Finally, the connection between the statistics of the most unstable eigenvalue and the emergent nonlinear dynamics is illustrated.

Coherent control of ultrafast quantum phenomena benefits from pulse-shaping capabilities allowing to modulate the envelope and instantaneous phase of optical fields on femtosecond timescales. While such control is available for optical fields, an analogy of a pulse shaper for freely propagating electrons is lacking. In this study, we theoretically demonstrate a method that enables near-arbitrary light-based shaping of electron wave packets in the time domain. The method is based on the quantum phase modulation of electron waves by coherent light with time-dependent frequency leading to generation of spectrally separated electron energy sidebands with shaped time-energy profiles and envelopes. Our results show that few-femtosecond time durations can be achieved without additional spectral broadening of the electron wave packet, allowing one to reach the combination of high time, spatial, and spectral resolutions in ultrafast imaging and diffraction experiments with pulsed electron beams.

Strongly correlated physics arises from electron-electron scattering within partially filled orbitals. Organic molecules in open-shell configurations are therefore good candidates to exhibit many-body effects. We focus on electron transport in a two-terminal single-molecule junction setup, in which the molecular bridge consists of an organic radical with a molecular orbital hosting a single unpaired electron [singly occupied molecular orbital (SOMO)]. We perform beyond state-of-the-art numerical simulations combining an ab initio description of the chemical environment with quantum field-theoretical techniques that account for many-body effects. The key observation is that the SOMO resonance is prone to splitting, and we identify a giant electronic scattering rate as the driving many-body mechanism, akin to that of the Mott metal-to-insulator transition. By comparing linear and cyclic radicals, we show that the spatial distribution of the SOMO and its projection on the molecular backbone have dramatic consequences for the transport properties of the junction. We argue that the phenomenon and the underlying microscopic mechanism apply to a broad family of open-shell molecular systems, and can explain puzzling experimental observations such as suppressed conductance in radical junctions.

We propose and experimentally demonstrate a detection method that significantly improves the precision of real-time measurement of the three-dimensional (3D) displacement of a levitated dipolar scatterer. Our technique relies on spatial mode sorting of the light scattered by the levitated object, allowing us to selectively extract the position information of all translational degrees of freedom with minimal losses. To this end, we collect all the light backscattered from a levitated nanoparticle using a parabolic mirror and couple it into a spatial mode sorter. We measure displacement sensitivities $(\sqrt{S_{\mathrm{imp},x}},\sqrt{S_{\mathrm{imp},y}},\sqrt{S_{\mathrm{imp},z}})=(1.7,2.4,1.0)\times {10}^{-14}\mathrm{m}/\sqrt{\mathrm{Hz}}$ below the zero-point motion $(x_{\mathrm{zpm}},y_{\mathrm{zpm}},z_{\mathrm{zpm}})=(2.2,2.4,1.6)\times {10}^{-12}\mathrm{m}$ of the levitated particle considered here. In the regime where environmental decoherence is not limited by gas collision, we estimate that our method can reach measurement efficiencies of $({\eta }_{\mathrm{tot}}^x,{\eta }_{\mathrm{tot}}^y,{\eta }_{\mathrm{tot}}^z)=(0.13,0.18,0.33)>\frac{1}{9}$, which would enable the 3D motional quantum ground state of a levitated optomechanical system.

Recent experiments with degenerate molecular gases dressed by elliptically polarized microwave fields have enabled new control of dipolar interactions via engineered anisotropy. We reveal a symmetry structure of the dipolar interaction that generates degeneracies among the interaction parameters, enabling a classification of spatial symmetries and equilibrium shapes of the gases. Exploiting these symmetries, we analyze solutions including beyond-mean-field quantum fluctuations in the dilute gas regime and develop a complementary variational theory. We map out the phase diagram of self-bound droplets and characterize their widths, energies, and densities.

A prediction makes a claim about a system's future given knowledge of its past. A retrodiction makes a claim about its past given knowledge of its future. The bidirectional machine is an ambidextrous hidden Markov chain that does both optimally by making explicit in its state structure all statistical correlations in a stochastic process. We introduce an informational taxonomy to profile these correlations via a suite of multivariate information measures. While prior results laid out the different kinds of information contained in isolated measurements, the associated informations were challenging to calculate explicitly. Overcoming this via bidirectional machine states, we expand that analysis to prediction and retrodiction. The result highlights fourteen interpretable and calculable measures that characterize a process's informational structure. In addition, we introduce a labeling and indexing scheme that systematizes information-theoretic analyses of complex multivariate systems. Operationalizing this, we provide algorithms to directly calculate all of these quantities in closed form for finitely modeled processes.