Electrochemical water splitting leads to variations in the electrolyte concentration which might impact the surface tension at both hydrogen and oxygen bubbles. Although the potentially resulting solutal Marangoni convection has been predicted, it has not yet been observed in experiments. To discover solutal Marangoni convection, the potential applied during the oxygen evolution reaction at microelectrodes, as well as the electrolyte concentration, have been systematically varied. To capture the bubble dynamics and local variations of the refractive index, a combination of high-speed shadowgraphy, particle tracking velocimetry, and schlieren imaging was employed. The analysis has identified an exotic regime of oxygen bubbles at high potentials, characterized by slow growth that is interrupted by intermittent current spikes, causing fast growth. While the fast growth is expectedly dominated by thermocapillary convection, the slow growth displays a vortical flow that circulates in the reverse direction at a much slower velocity. This flow pattern is attributed to an increased sulfuric acid concentration at the bubble foot, induced by proton generation during oxygen evolution. This locally elevates the surface tension and causes the solutal Marangoni flow. These findings confirm the theoretical prediction of solutal effects in the bubble dynamics during water electrolysis and reveal the fascinating physics behind.

A parametrically driven oscillator has two stable vibrational states at half the modulation frequency. The states have opposite phases and equal amplitudes. An extra drive at half the modulation frequency provides an effective bias that lifts the state symmetry. Quantum fluctuations lead to switching between the states, i.e., to phase-flip transitions. We develop a semiclassical approach that allows us to find the dependence of the switching rates on the amplitude of the bias and the parameters of the modulating field. We find that the rate of switching from a “shallow” state can become anomalously small at certain parameter values, leading to an efficient localization in this state. This is a consequence of the change of the topology of the oscillator phase trajectories. The results pave the way for implementing nonreciprocal quantum Ising systems based on parametric oscillators.

A theoretical model is proposed for the formation of ultracold ground-state triatomic molecules in weakly bound energy levels. The process is driven by the electric component of a microwave field, which induces the association of an ultracold atom colliding with an ultracold diatomic molecule. This model is exemplified using ${}^{39}\mathrm{K}$ atoms and ${}^{23}\mathrm{Na}{}^{39}\mathrm{K}$ molecules, both in their ground states, a scenario of experimental relevance. The model assumes that the dynamics of the association are dominated by the long-range van der Waals interaction between ${}^{39}\mathrm{K}$ and ${}^{23}\mathrm{Na}{}^{39}\mathrm{K}$. The electric microwave association mechanism relies on the intrinsic electric dipole moment of ${}^{23}\mathrm{Na}{}^{39}\mathrm{K}$, which drives transitions between its lowest rotational levels ($j$=0 and $j$=1). The energies of the uppermost triatomic energy levels are computed by numerically solving coupled Schrödinger equations using the Mapped Fourier Grid Hamiltonian method. Measurable association rates are derived within the framework of a perturbative approach. This method of electric microwave association provides an alternative to atom-molecule association via magnetic Feshbach resonances for forming ultracold, deeply bound triatomic molecules, and is applicable to a wide range of polar diatomic molecules.

We present a hardware-reconfigurable ansatz on $N_q$-qubits for the variational preparation of many-body states of the Anderson impurity model (AIM) with $N_{\text{imp}}+N_{\text{bath}}=N_q/2$ sites, which conserves total charge and spin $z$ component within each variational search subspace. The many-body ground state of the AIM is determined as the minimum over all minima of $O\left(N_q^2\right)$ distinct charge-spin sectors. Hamiltonian expectation values are shown to require $\omega \left(N_q\right)<N_{\text{meas.}}\le O\left(N_{\text{imp}}{N}_{\text{bath}}\right)$ symmetry-preserving, parallelizable measurement circuits, each amenable to postselection. To obtain the one-particle impurity Green's function we show how initial Krylov vectors can be computed via midcircuit measurement and how Lanczos iterations can be computed using the symmetry-preserving ansatz. For a single-impurity Anderson model with a number of bath sites increasing from one to seven, we show using numerical emulation that the ease of variational ground-state preparation is suggestive of linear scaling in circuit depth and subquartic scaling in optimizer complexity. We therefore expect that, combined with time-dependent methods for Green's function computation, our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors. Finally, with a view towards computing real materials properties of interest like magnetic susceptibilities and electron-hole propagators, we provide a straightforward method to compute many-body, time-dependent correlation functions using a combination of time evolution, midcircuit measurement-conditioned operations, and the Hadamard test.

This paper introduces a comprehensive extension of the path-integral formalism to model stochastic processes with arbitrary multiplicative noise. To do so, the Itô diffusive process is generalized by incorporating a multiplicative noise term $\left[\eta \right(t\left)\right]$ that affects the diffusive coefficient in the stochastic-differential equation. Then, using the Parisi-Sourlas method, we estimate the transition probability between states of a stochastic variable $\left[X\right(t\left)\right]$ based on the cumulant-generating function $\left({\mathcal{K}}_{\eta }\right)$ of the noise. A parameter $\gamma \in [0,1]$ is introduced to account for the type of stochastic calculation used and its effect on the Jacobian of the path-integral formalism. Next, the Feynman-Kac functional is employed to derive the Fokker-Planck equation for generalized Itô diffusive processes, addressing issues with higher-order derivatives and ensuring compatibility with known functionals such as Onsager-Machlup and Martin-Siggia-Rose-Janssen-De Dominicis in the white-noise case. The general solution for the Fokker-Planck equation is provided when the stochastic drift is proportional to the diffusive coefficient and ${\mathcal{K}}_{\eta }$ is scale invariant. Finally, the Brownian motion (BM), the geometric Brownian motion (GBM), the Levy $\alpha$-stable flight $\left[\text{LF}\left(\alpha \right)\right]$, and the geometric Levy $\alpha$-stable flight $\left[\text{GLF}\left(\alpha \right)\right]$ are simulated with thresholds, providing analytical comparisons for the probability density, Shannon entropy, and entropy production rate. It is found that restricted BM and restricted GBM exhibit quasisteady states since the rate of entropy production never vanishes. It is also worth mentioning that in this work, the GLF($\alpha )$ is defined and it is shown that its solution is found without the need for Itô's lemma.

The evidence indicates that intracranial electroencephalographic connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture nontrivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1 score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting.

To leverage the full potential of quantum error-correcting stabilizer codes it is crucial to have an efficient and accurate decoder. Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. In this work, we explore a model-free, data-driven, approach to decoding, using a graph neural network (GNN). The decoding problem is formulated as a graph classification task in which a set of stabilizer measurements is mapped to an annotated detector graph for which the neural network predicts the most likely logical error class. We show that the GNN-based decoder can outperform a matching decoder for circuit level noise on the surface code given only the simulated data, while the matching decoder is given full information of the underlying error model. Although training is computationally demanding, inference is fast and scales approximately linearly with the space-time volume of the code. We also find that we can use large, but more limited, datasets of real experimental data for the repetition code, giving decoding accuracies that are on par with minimum weight perfect matching. The results show that a purely data-driven approach to decoding may be a viable future option for practical quantum error correction, which is competitive in terms of speed, accuracy, and versatility.

Primordial black holes (PBHs) may form before cosmological first-order phase transitions, leading to inevitable collisions between PBHs and bubble walls. In this paper, we have simulated the coevolution of an expanding scalar wall passing through a black hole with full numerical relativity. This black hole–bubble wall collision yields multiple far-reaching phenomena, including the PBH mass growth, gravitational wave radiations, and momentum recoil that endows PBHs with additional velocities, approximately doubling the formation rate for PBH binaries and hence strengthening the observational constraints on the PBH abundances.

Reliably predicting nuclear properties across the entire chart of isotopes is important for applications ranging from nuclear astrophysics to superheavy science to nuclear technology. To this day, however, all the theoretical models that can scale at the level of the chart of isotopes remain semiphenomenological. Because they are fitted locally, their predictive power can vary significantly; different versions of the same theory provide different predictions. Bayesian model mixing takes advantage of such imperfect models to build a local mixture of a set of models to make improved predictions. Earlier attempts to use Bayesian model mixing for mass table calculations relied on models treated at single-reference energy density functional level, which fail to capture some of the correlations caused by configuration mixing or the restoration of broken symmetries. In this study we have applied Bayesian model mixing techniques within a multireference energy density functional (MR-EDF) framework. We considered predictions of two-particle separation energies from particle number projection or angular momentum projection with four different energy density functionals—a total of eight different MR-EDF models. We used a hierarchical Bayesian stacking framework with a Dirichlet prior distribution over weights together with an inverse log-ratio transform to enable positive correlations between different models. We found that Bayesian model mixing provides significantly improved predictions compared to the participating models.

We investigate the directional characteristics of photon statistics in dimers of quantum emitters. For their analysis, we construct a two-point second-order correlation function that allows us to find an alternative mechanism for photon anticorrelation, termed as geometric antibunching. This phenomenon is completely agnostic to the quantum state of the emitters and emerges from quantum interference effects due to the indistinguishability of different two-photon optical pathways. Finally, we explore its occurrence in emitters placed in the vicinity of a flat substrate and a nanosphere, demonstrating its tunability through the different material and geometric parameters of these devices.

We introduce a family of (semi)-bold-line series, assisted with $1/N_f$ expansions, with $N_f$ being the number of fermion flavors. If there is no additional $N_f$ cut, the series reduces to the random phase approximation series in the density-density channel, complementary to the particle-hole and particle-particle channels introduced in Phys. Rev. B 102, 195122 (2020). To address the very localized integrands in diagrammatic Monte Carlo, we introduced an innovative VEGAS-RG-MCMC sampling method, where we found a significant decrease of autocorrelation time without the usage of the state-of-the-art many-configuration MCMC (MCMCMC) method while the combination of both is also straightforward. We performed extensive benchmarks for density, energy, and pressure with the $t\text{−}{t}^{\prime } \mathrm{SU}\left(N_f\right)$ Hubbard model on a square lattice and honeycomb lattices over a wide range of numerical methods. For benchmark purposes, we also implement bare-$\mathrm{U}$ symmetry-broken perturbation series for the two-dimensional SU(2) Hubbard model on the honeycomb lattice, where we found encouraging results from weak to intermediate couplings.

Solar Orbiter observations are used to investigate coherent structure prevalence in turbulent solar wind flows in situ from $\sim 1$ to 0.4 astronomical units (au). Magnetic field fluctuations are obtained by wavelet decompositions which preferentially resolve either signatures of coherent structures or wave packets. We find a single fluctuation threshold in each of the kinetic and inertial ranges above which coherent structures typically dominate. It is insensitive to plasma parameters or solar distance, that is, different states of the turbulence, suggesting a ubiquitous constraint on the turbulent phenomenology.

We employ time-resolved optical reflectivity to investigate the ultrafast dynamics of the charge-transfer gap (CTG) in a parent cuprate compound $\mathrm{C}{\mathrm{a}}_2\mathrm{Cu}{\mathrm{O}}_2\mathrm{C}{\mathrm{l}}_2$ (CCOC). We observe a persistent photoinduced red shift of the CTG that lasts up to 1000 ps. The red shift during the slow decay after 10 ps can be well modeled by the localized picture, whereas its maximum value at $\sim 0.9$ ps involves additional contribution from the renormalization of the Hubbard $U$ due to screening effect from delocalized electrons. Furthermore, coupling between the midgap absorption and a slow acoustic phonon launches coherent oscillations below the CTG, observed as a $\sim 20$ GHz modulation with a dispersion independent of the pump fluence. These results demonstrate the tunning of the CTG by light, unveil complex interplay between multiple degrees of freedom, and contribute to a deeper understanding of superconductivity and correlated materials.

Semiconductor-superconductor hybrid nanocircuits are of high interest because of their potential applications in quantum computing. Semiconductors with strong spin-orbit coupling and large $g$ factor are particularly attractive since they are the basic building blocks of novel qubit architectures. However, for the engineering of these complex circuits, the building blocks must be characterized in detail. We have investigated a Josephson junction where the weak link is a two-dimensional electron gas (2DEG) hosted in an InAs/InGaAs heterostructure grown on a GaAs substrate. We employed the in situ epitaxially grown Al layer as a superconducting contact to form an rf SQUID, and also to create a microwave resonator for sensing the Josephson inductance. We determined the gate-dependent current-phase relation and observed supercurrent interference in out-of-plane magnetic fields. With the application of an in-plane magnetic field, we induced asymmetry in the interference pattern, which was found to be anisotropic in the device plane.