Dissipation of quantum vortex motion is fundamental to superfluid dynamics and quantum turbulence, yet there is currently a large gap between theory and experiments with ultracold atoms. Here we present a microscopic open quantum systems theory of thermally damped vortex motion in oblate atomic superfluids that includes previously neglected energy-damping interactions between superfluid and thermal atoms. This mechanism couples strongly to vortex core motion and causes dissipation of vortex energy due to mutual friction, as well as Brownian motion of vortices due to thermal fluctuations. We derive an analytic expression for the dimensionless mutual friction coefficient that gives excellent quantitative agreement with experimentally measured values, without any fitted parameters. Our work closes an existing two orders of magnitude gap between dissipation theory and experiments, previously bridged by fitted parameters, and provides a microscopic origin for the mutual friction and diffusion of quantized vortices in two-dimensional atomic superfluids.

Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than 100, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum systems can successfully be simulated on these devices remains unclear. Here, we report on numerical results of physics-motivated variational ansatzes, as well as cloud simulations performed on several of IBM's superconducting quantum computers to simulate ground states of spin chains having a wide range of system sizes up to 102 qubits. Our numerical analysis shows that the accuracy of the ground-state energy and fidelity improves substantially by increasing the number of layers used in the ansatzes. From the cloud experiments, we find that the ground-state energies extracted from realizations across different quantum computers and system sizes reach the expected values to within errors that are small (i.e., on the percent level), including the inference of the energy density in the thermodynamic limit from these values. We achieve this accuracy through a combination of physics-motivated variational ansatzes, and efficient, scalable energy-measurement and error-mitigation protocols, including the use of a reference state in the zero-noise extrapolation. By using a 102-qubit system, we have been able to successfully apply up to 3186 CNOT gates in a single circuit when performing gate-error mitigation. Our accurate, error-mitigated results for random parameters in the ansatz states suggest that a standalone hybrid quantum-classical variational approach for large-scale $XXZ$ models considered in this work is feasible.

We measure the temperature dependence of the indirect band gap of isotopically purified ${}^{28}\mathrm{Si}$:P in the regime from 0.1 K to 3 K by high-resolution absorption spectroscopy of the donor bound exciton transition. The measurements increase the up-to-date precision of the temperature-dependent band gap change by more than one order of magnitude and reveal a ${\mathrm{T}}^4$ dependence which is about a factor of two less than observed in previous measurements. Such a ${\mathrm{T}}^4$ dependence is predicted by theory, but the absolute values differ between our experiment and the most up-to-date calculations by a factor of 30, corroborating that the electron-phonon interaction at low temperatures is still not correctly included into theory. What is more, the ability of such very high-precision band-gap measurements facilitates the use of time- and spatially resolved ${}^{28}\mathrm{Si}$:P absorption as a contactless, local thermometer and electric field sensor with a demonstrated time resolution of milliseconds.

The “hierarchical equations of motion” (HEOM) method is a powerful exact numerical approach to solve the dynamics and find the steady-state of a quantum system coupled to a non-Markovian and nonperturbative environment. Originally developed in the context of physical chemistry, it has also been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. Here we present a numerical library in Python, integrated with the powerful QuTiP platform, which implements the HEOM for both bosonic and fermionic environments. We demonstrate its utility with a series of examples consisting of benchmarks against important known results and examples demonstrating insights gained with this library for this article. For the bosonic case, our results include demonstrations of how to fit arbitrary spectral densities with different approaches, and a study of the dynamics of energy transfer in the Fenna-Matthews-Olson photosynthetic complex. For the latter, we both clarify how a suitable non-Markovian environment can protect against pure dephasing, and model recent experimental results demonstrating the suppression of electronic coherence. Importantly, we show that by combining the HEOM method with the reaction coordinate method we can observe nontrivial system-environment entanglement on timescales substantially longer than electronic coherence alone. We also demonstrate results showing how the HEOM can be used to benchmark different strategies for dynamical decoupling of a system from its environment, and show that the Uhrig pulse-spacing scheme is less optimal than equally spaced pulses when the environment's spectral density is very broad. For the fermionic case, we present an integrable single-impurity example, used as a benchmark of the code, and a more complex example of an impurity strongly coupled to a single vibronic mode, with applications to single-molecule electronics.

As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty-aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the x-ray absorption spectra of small molecules, as well as their pointwise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large-scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties.

We propose a magnetic topological transistor based on ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$, in which the “on” state (quantized conductance) and the “off” state (zero conductance) can be easily switched by changing the relative direction of two adjacent electric fields (parallel vs antiparallel) applied within a two-terminal junction. We explain that the proposed magnetic topological transistor relies on a novel mechanism due to the interplay of topology, magnetism, and layer degrees of freedom in ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$. Its performance depends substantially on film thickness and type of magnetic order. We show that “on” and “off” states of the transistor are robust against disorder due to the topological nature of the surface states. Our work opens an avenue for applications of layer-selective transport based on the topological van der Waals antiferromagnet ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$.

Topological superconductors are predicted to exhibit outstanding phenomena, including non-Abelian anyon excitations, heat-carrying edge states, and topological nodes in the Bogoliubov spectra. Nonetheless, and despite major experimental efforts, we are still lacking unambiguous signatures of such exotic phenomena. In this context, the recent discovery of coexisting superconductivity and ferroelectricity in lightly doped and ultraclean ${\mathrm{SrTiO}}_3$ opens new opportunities. Indeed, a promising route to engineer topological superconductivity is the combination of strong spin-orbit coupling and inversion-symmetry breaking. Here we study a three-dimensional parabolic band minimum with Rashba spin-orbit coupling, whose axis is aligned by the direction of a ferroelectric moment. We show that all of the aforementioned phenomena naturally emerge in this model when a magnetic field is applied. Above a critical Zeeman field, Majorana-Weyl cones emerge regardless of the electronic density. These cones manifest themselves as Majorana arc states appearing on surfaces and tetragonal domain walls. Rotating the magnetic field with respect to the direction of the ferroelectric moment tilts the Majorana-Weyl cones, eventually driving them into the type-II state with Bogoliubov Fermi surfaces. We then consider the consequences of the orbital magnetic field. First, the single vortex is found to be surrounded by a topological halo and is characterized by two Majorana zero modes: one localized in the vortex core and the other on the boundary of the topological halo. Based on a semiclassical argument we show that upon increasing the field above a critical value the halos overlap and eventually percolate through the system, causing a bulk topological transition that always precedes the normal state. Finally, we propose concrete experiments to test our predictions.

We discover transitions characterized by distinguishability of bosons in nonunitary dynamics with parity-time $\left(\mathcal{PT}\right)$ symmetry. We show that $\mathcal{PT}$-symmetry breaking, a unique transition in non-Hermitian open systems, enhances regions in which bosons can be regarded as distinguishable. This means that classical computers can sample the boson distributions efficiently in these regions by sampling the distribution of distinguishable particles. In a $\mathcal{PT}$-symmetric phase, we find one dynamical transition upon which the distribution of bosons deviates from that of distinguishable particles, when bosons are initially put at distant sites. If the system enters a $\mathcal{PT}$-broken phase, the threshold time for the transition is suddenly prolonged, since dynamics of each boson is diffusive (ballistic) in the $\mathcal{PT}$-broken ($\mathcal{PT}$-symmetric) phase. Furthermore, the $\mathcal{PT}$-broken phase also exhibits a notable dynamical transition on a longer time scale, at which the bosons again become distinguishable. This transition, and hence the classical easiness of sampling bosons in long times, is true for generic postselected nonunitary quantum dynamics, while it is absent in unitary dynamics of isolated quantum systems. $\mathcal{PT}$-symmetry breaking can also be characterized by the efficiency of a classical algorithm based on the rank of matrices, which can (cannot) efficiently compute the photon distribution in the long-time regime of the $\mathcal{PT}$-broken ($\mathcal{PT}$-symmetric) phase.

Realizing scalable quantum networks requires a meticulous level of understanding and mitigating the deleterious effects of decoherence. Many quantum device platforms feature multiple decoherence mechanisms, often with a dominant mechanism seemingly fully masking others. In this paper, we show how access to weaker dephasing mechanisms can nevertheless be obtained for optically active qubits by performing two-photon coincidence measurements. To this end we theoretically investigate the impact of different decoherence mechanisms on cooperatively emitting quantum dots. Focusing on the typically dominant deformation-potential coupling to longitudinal acoustic phonons and typically much less severe additional sources of pure dephasing, we employ a numerically exact method to show that these mechanisms lead to very different two-photon coincidence signals. Moreover, surprisingly, the impact of the strongly coupled phonon environment is weak and leads to long-lived coherences. We trace this back to the superohmic nature of the deformation-potential coupling causing interemitter coherences to converge to a nonzero value on a short timescale, whereas pure dephasing contributions cause a complete decay of coherence over longer times. Our approach provides a practical means of investigating decoherence processes on different timescales in solid-state emitters, and thus contributes to understanding and possibly eliminating their detrimental influences.

Tentacles on soft corals exhibit intriguing spatiotemporal dynamics of motions, which may benefit their survival and fitness. Despite their significance, studies of their quantitative properties still remain challenging and unexplored. Such motions are characterized by coherent patterns across both space and time, yet computational methods that address spatiotemporal dynamics are rare. Here, we introduce a data-driven method called dynamic mode decomposition (DMD) to explore the spatiotemporal behavior of tentacles of Anthelia glauca, where the motions of eight tentacles are captured by stereovision and object tracking techniques. The DMD reveals the stochastic motions of the tentacles, which can be well modeled as $1/f$-type motion. Additionally, the pulsation behaviors of our soft corals are also captured by analyzing the DMD spectrum and the sliding-window DMD, where these behaviors emerge as spatial DMD modes with increased power. Finally, the impact of light conditions on the $1/f$-type motion and pulsation behaviors is explored, where certain light conditions can manipulate the $1/f$-type motion and emergence of pulsation behaviors. Our work, combining experimental observation and a data-driven method to characterize spatiotemporal motions of coral tentacles, paves the way to exploring the complex behaviors of individual organisms and colonies, and the effect from changing environmental variables.

Using a combination of bound-state spectroscopy and loss spectroscopy, we pinpoint eight intrastate Feshbach resonances in ${}^{39}\mathrm{K}$, as well as six previously unexplored interstate ones. We perform a detailed characterization of four of the intrastate resonances and two of the interstate ones. We carry out coupled-channel scattering calculations and find good agreement with experiment. The combination of experiment and theory provides a faithful map of the scattering length $a$ and permits precision measurements of the signatures of Efimov physics across four intermediate-strength intrastate resonances. We measure the modulation of the $a^4$ scaling of the three-body loss coefficient for both $a<0$ and $a>0$, as well as the many-body loss dynamics at unitarity (where $a$ diverges). The absolute positions of the observed Efimov features confirm a ubiquitous breakdown of Efimov–van der Waals universality in ${}^{39}\mathrm{K}$, while their relative positions are in agreement with the universal Efimov ratios. The loss dynamics at unitarity are the same (within experimental uncertainties) for the three broadest Feshbach resonances, consistent with observing little variation in the widths of the corresponding Efimov features.

Classic models of cell size control consider that cells divide when reaching a threshold, e.g., size, age, or size extension. The molecular basis of the threshold involves multiple layers of regulation as well as gene noise. In this paper, we study the cell cycle as a first-passage problem with a stochastic threshold and discover that such stochasticity affects the intergeneration statistics, which bewilders the criteria to distinguish the types of size control models. The analytic results show that the autocorrelation in the threshold can drive a sizer model to the adderlike and even timerlike intergeneration statistics, which is supported by simulations. Following the picture that the autocorrelation in the threshold can propagate to the intergeneration statistics, we further show that the adder model can be driven to the timerlike one by a positively autocorrelated threshold, and even to the sizerlike one when the threshold is negatively autocorrelated. This work highlights the importance of examining gene noise in size control.

Dissipative Kerr solitons (DKSs) in microresonators have boosted the development of the chip-scale ultrastable microcomb sources, and thrived in both fundamental physics and a wide range of applications. Among various DKS states, single DKS, double DKS, and a perfect soliton crystal could be identified simply based on the optical spectrum. Especially, the double DKS state, due to its two-pulse-interference nature, has recently found its own application in microwave photonics, such as reconfigurable rf filters. However, the traditional method to generate double DKS usually yields stochastically relative positions of the DKS in the cavity, which limits the versatility of the application. Here a method to deterministically generate double DKSs with fixed relative positions assisted by the dual-pump driven scheme in a 97-GHz ${\mathrm{Si}}_3{\mathrm{N}}_4$ microresonator is demonstrated. Via the dual-pump scheme, not only has the single soliton been repeatably generated, double DKS with deterministic relative positions has also been realized through backward tuning. The effects of pump wavelength tuning and pump power on the relative positions are investigated. A direct bridge between the relative positions and the avoided mode crossing induced modulated cw background is established. This work not only provides insight to DKS dynamics in a dual-pump scheme, but also improves the versatility of double-DKS based applications in microwave photonics.

Active matter systems provide fascinating examples of pattern formation and collective motility without counterpart in equilibrium systems. Here, we employ Brownian dynamics simulations to study the collective motion and self-organization in systems of self-propelled semiflexible filaments, inspired by the gliding motility of filamentous Cyanobacteria. Specifically, we investigate the influence of stochastic direction reversals on the patterns. We explore pattern formation and dynamics by modulating three relevant physical parameters, the bending stiffness, the activity, and the reversal rate. In the absence of reversals, our results show rich dynamical behavior including spiral formation and collective motion of aligned clusters of various sizes, depending on the bending stiffness and self-propulsion force. The presence of reversals diminishes spiral formation and reduces the sizes of clusters or suppresses clustering entirely. This homogenizing effect of direction reversals can be understood as reversals providing an additional mechanism to either unwind spirals or to resolve clusters.

An important issue for the origins of life is ensuring the accurate maintenance of information in replicating polymers in the face of inevitable errors. Here, we investigated how this maintenance depends on reaction kinetics by incorporating the elementary steps of polymerization into the population dynamics of polymers. We found that template-directed polymerization entails an inherent error-correction mechanism akin to kinetic proofreading, potentially generating the tolerance of long polymers to an error catastrophe at the cost of a slow polymerization process. As this mechanism does not require enzymes, it is likely to operate under broad prebiotic conditions.

Dirac spin liquids (DSLs) are gapless featureless states, yet interesting by virtue of the effective field theory describing them—(2 + 1)-dimensional quantum electrodynamics (${\mathrm{QED}}_3$). Further, a DSL is known to be a “parent state” of various seemingly unrelated ordered states, such as antiferromagnets and valence bond solids in the sense that one can obtain ordered states by condensing magnetic monopoles of the emergent gauge field. Can operators in the effective field theory, such as the emergent electric field, be externally induced and measured? In this work, we exploit the parent state picture to argue that the answer is yes. We propose a range of “monopole Josephson effects” that arise when two ordered states are separated by a region of the parent DSL. In particular, we show that one can induce an AC monopole Josephson effect, which manifests itself as an AC emergent electric field in the spin liquid, accompanied by a measurable spin current. Further, we show that this AC emergent electric field can be measured as a sharp tunable peak in Raman scattering. This work provides a theoretical proof of principle that emergent gauge fields in spin liquids can be externally induced, manipulated, and probed using more conventional states, which offers a generic platform for studying the exotic spin phases.

The electromagnetic emission resulting from neutron star mergers have been shown to encode properties of the ejected material in their light curves. The ejecta properties inferred from the kilonova emission has been in tension with those calculated based on the gravitational wave signal and numerical relativity models. Motivated by this tension, we construct a broad set of surrogate light curve models derived for kilonova ejecta. The four-parameter family of two-dimensional anisotropic simulations and its associated surrogate explore different assumptions about the wind outflow morphology and outflow composition, keeping the dynamical ejecta component consistent. We present the capabilities of these surrogate models in interpolating kilonova light curves across various ejecta parameters and perform parameter estimation for AT2017gfo both without any assumptions on the outflow and under the assumption that the outflow must be representative of solar $r$-process abundance patterns. Our parameter estimation for AT2017gfo shows these surrogate models help alleviate the ejecta property discrepancy while also illustrating the impact of systematic modeling uncertainties on these properties, urging further investigation.