The topological phases of monolayer jacutingaite (${\mathrm{Pt}}_2{\mathrm{HgSe}}_3$) under off-resonance high-frequency and high-intensity laser irradiation and staggered sublattice potential $V$ are investigated. The various steady-state phases are realized by an appropriate choice of the off-resonance circularly polarized laser field parameters and the staggered sublattice potential. By tuning the band gap through $V$ and/or laser intensity, a phase diagram is deduced and explained. Increasing the laser intensity at $V=0$ leads to a photoinduced quantum Hall insulator state with chiral edge currents, while increasing $V$ in the absence of laser field changes the phase from quantum spin Hall to quantum valley Hall phases. An electromagnetically induced, steady-state spin-polarized quantum Hall state is predicted at particular values of $V$ and the laser intensity relative to the spin-orbit coupling strength. The emergence of the topological phases is corroborated by calculating the Chern numbers of each band, and the Hall conductivity at zero temperature. Calculations of the Nernst coefficient give further evidence at nonzero temperatures. Along the phase boundaries, we observe steady-state single Dirac cone, spin-polarized, and spin-valley-polarized metal states which may be exploited in use of jacutingaites in spin and valleytronics.

We propose a method for detecting single chiral phonons that will enable their use as dark-matter detectors. We suggest metal–organic frameworks (MOFs) as detector materials, as their flexibility yields low-energy chiral phonons with measurable magnetic moments, and their anisotropy leads to directional sensitivity, which mitigates background contamination. To demonstrate our proposal, we calculate the phononic structure of the MOF ${\mathrm{InF}}_3(4,4^{\prime }$-bipyridine), and show that it has highly chiral acoustic phonons. Detection of such chiral phonons via their magnetic moments would dramatically lower the excitation energy threshold for dark matter detection to the energy of a single phonon. We show that single-phonon detection in a MOF would extend detector reach ten or more orders of magnitude below current limits, enabling exploration of a multitude of as-yet-unprobed dark matter candidates.

Efficient decoding to estimate error locations from outcomes of syndrome measurements is the prerequisite for quantum error correction. Decoding in the presence of circuit-level noise including measurement errors should be considered in case of actual quantum computing devices. In this paper, we develop a decoder for circuit-level noise that solves the error estimation problems as Ising-type optimization problems. We confirm that the threshold theorem in the surface code under circuit-level noise is reproduced with an error threshold of approximately 0.4%. We also demonstrate the advantage of the decoder through which the $Y$ error detection rate can be improved compared with other matching-based decoders. Our results reveal that a lower logical error rate can be obtained using our algorithm compared with that of the minimum-weight perfect matching algorithm.

Graph states are entangled states that are useful for several quantum information processing tasks, such as measurement-based quantum computation and quantum metrology. As the size of graph states realized in experiments increases, it becomes more essential to devise efficient methods for estimating the fidelity between the ideal graph state and an experimentally realized actual state. Any efficient fidelity-estimation method, in general, must use multiple experimental settings, i.e., needs to switch between at least two measurements. Recently, it has been shown that a single measurement is sufficient if the noise can be modeled as the phase-flip error. Since the bit-flip error should also occur in several experiments, it is desired to extend this simplest method to noise models that include phase and bit-flip errors. However, it seems to be nontrivial because their result strongly depends on properties of the phase-flip error. In this paper, by analyzing effects of the bit-flip error on stabilizer operators of graph states, we achieve the extension to the depolarizing noise, which is a major noise model including phase and bit-flip errors. We also numerically evaluate our simplest method for noise models interpolating between the phase-flip and depolarizing noises.

We propose a single-photon-by-single-photon all-optical switch concept based on interference-localized states on lattices and their delocalization by interaction. In its “open” operation, the switch stops single photons while allows photon pairs to pass the switch. Alternatively, in the “closed” operation, the switch geometrically separates single-photon and two-photon states. We demonstrate the concept using a three-site Stub unit cell and the diamond chain. The systems are modeled by Bose-Hubbard Hamiltonians, and the dynamics is solved by exact diagonalization with Lindblad master equation. We discuss realization of the switch using photonic lattices with nonlinearities, superconductive qubit arrays, and ultracold atoms. We show that the switch allows arbitrary “ON”/“OFF” contrast while achieving picosecond switching time at the single-photon switching energy with contemporary photonic materials.

In this paper we explore the two-dimensional Green's function problem above a spatiotemporally modulated loaded wire media. We suggest a spatiotemporal spectral representation to the space-time domain Green's function. Then, we evaluate the spectral representation both by a brute-force numerical integration as well as in a physics-guided fashion by deforming the integration path along the steepest descent path and encircling singular points in the complex spectral plane. The latter approach leads in general to three isolated wave contributions: a refracted ray due to a saddle point contribution, a head-wave-like due to the branch points, and leaky modes that are associated with pole singularities. This way, we identify different wave species that are all subject to a synthetic motion that is effectively caused by the spatiotemporal modulation and give rise to unique nonreciprocal behavior. Lastly, we provide asymptotic closed form expressions for the various waves that comprise the Green's function, as well as conditions under which they contribute.

High-gain free-electron lasers (FELs) are becoming important light sources at short wavelengths such as the EUV and x-ray regimes. A particularly promising concept is the regenerative amplifier FEL (RAFEL), which can greatly increase the brightness and stability of a single pass device. One of the critical challenges of the x-ray RAFEL is maintaining electron-optical overlap over the relatively large (hundreds of meters) footprint of the system. Numerical modeling of x-ray RAFELs with angular and positional errors is critical for designing stable cavities, as well as to predict signatures of specific misalignment effects. Full-scale simulations of x-ray FELs are incredibly time consuming, making large-scale parameter searches intractable on reasonable timescales. In this paper, we present a semi-analytical model that allows to investigate realistic scenarios—x-ray cavity without gain (“cold cavity” or x-ray FEL oscillator) and x-ray RAFEL—in the presence of angular/positional errors and electron trajectory oscillation. We especially focus on fast modeling of the FEL process and x-ray optics, while capturing effects pertaining to actual experimental setups at the Linac Coherent Light Source (LCLS) at SLAC. Such a method can be used to explore RAFEL at other wavelengths by suitable replacement of the optics modeling.

We use a formalism that describes electron ejections from graphenelike targets by dark matter (DM) scattering for general forms of scalar and spin-1/2 DM-electron interactions in combination with state-of-the-art density functional calculations to produce predictions and reach estimates for various possible carbon-based detector designs. Our results indicate the importance of a proper description of the target electronic structure. In addition, we find a strong dependence of the predicted observed signal for different DM candidate masses and interaction types on the detailed geometry and design of the detector. Combined with directional background vetoing, these dependencies will enable the identification of DM particle properties once a signal has been established.

We develop a formalism to describe electron ejections from graphenelike targets by dark matter (DM) scattering for general forms of scalar and spin-1/2 DM-electron interactions, and we compare their applicability and accuracy within the density functional theory (DFT) and tight-binding (TB) approaches. This formalism allows for accurate prediction of the daily modulation signal expected from DM in upcoming direct detection experiments employing graphene sheets as the target material. A key result is that the physics of the graphene sheet and that of the DM and the ejected electron factorize, allowing for the rate of ejections from all forms of DM to be obtained with a single graphene response function. We perform a comparison between the TB and DFT approaches to modeling the initial state electronic wave function within this framework, with DFT emerging as the more self-consistent and reliable choice due to the challenges in the embedding of an appropriate atomic contribution into the TB approach.

To understand how the long connections of a brain functional network come from the short connections of its corresponding structural network, remote synchronization (RS) was recently studied in star graph networks. However, the motif of the star graph cannot completely characterize the features of brain networks as the leaf nodes of a star graph may also be connected to each other to some extent in real brain networks. Especially, the dynamics of a star motif in a brain network will be seriously influenced by its surrounding nodes, i.e., other parts of the brain network. To study RS of real brain networks, we here present a model of star-like networks by considering both the partial connections among leaf nodes and the influence of other parts of the brain network. We find that RS will not appear in all leaf nodes and instead appears only in the group of indirectly connected leaf nodes when the frequency difference between the hub and leaf nodes is not large enough, resulting in the concept of partial RS (PRS). Further, we find that the partial connections among leaf nodes favor PRS, implying that PRS can more easily appear in real brain networks than RS and thus provides a different way to understand the mechanism of long connections in brain functional networks. Moreover, we find another kind of PRS, i.e., double PRS, and discuss the dependence of PRS on system parameters. Finally, a brief theoretical analysis is provided to explain the results.

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability. Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times, thereby accessing a long-horizon forecasting regime well beyond classical methods. We find that, in this regime, accuracy decorrelates with classical invariant measures of predictability like the Lyapunov exponent. However, in data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.

Nonequilibrium processes break time-reversal symmetry and generate entropy. Living systems are driven out-of-equilibrium at the microscopic level of molecular motors that exploit chemical potential gradients to transduce free energy to mechanical work, while dissipating energy. The amount of energy dissipation, or the entropy production rate (EPR), sets thermodynamic constraints on cellular processes. Practically, calculating the total EPR in experimental systems is challenging due to the limited spatiotemporal resolution and the lack of complete information on every degree of freedom. Here, we propose an inference approach for a tight lower bound on the total EPR given partial information, based on an optimization scheme that uses the observed transitions and waiting times statistics. We introduce hierarchical bounds relying on the first- and second-order transitions, and the moments of the observed waiting time distributions, and apply our approach to two generic systems of a hidden network and a molecular motor, with lumped states. Finally, we show that a lower bound on the total EPR can be obtained even when assuming a simpler network topology of the full system.

Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages over classical counterparts in practical learning tasks is yet to be demonstrated. A promising avenue to explore potential advantages is the learning of data generated by quantum mechanical systems and presented in an inherently quantum mechanical form. In this article, we explore the applicability of quantum data learning to practical problems in high-energy physics, aiming to identify domain specific use-cases where quantum models can be employed. We consider quantum states governed by one-dimensional lattice gauge theories and a phenomenological quantum field theory in particle physics, generated by digital quantum simulations or variational methods to approximate target states. We make use of an ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states in the Schwinger model, (de)confinement phases from time-evolved states in the ${\mathbb{Z}}_2$ gauge theory, and that it can extract fermion flavor/coupling constants in a quantum simulation of parton shower. The observation of nontrivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum data learning architecture in high-energy physics.

We use a tensor network renormalization group method to study random $S=2$ antiferromagnetic Heisenberg chains with alternating bond strength distributions. In the absence of randomness, an imposed dimerization with bond alternation induces two quantum critical points between the $S=2$ Haldane phase, a partially dimerized phase, and a fully dimerized phase, depending on the strength of dimerization. These three phases, called ($\sigma ,4-\sigma$) = (2,2), (3,1), and (4,0) phases, are valence-bond solid (VBS) states characterized by $\sigma$ valence bonds (effective spin-1/2 singlet pairs) forming across even links and $4-\sigma$ valence bonds on odd links. Here we study the effects of bond randomness on the ground states of the dimerized spin chain, calculating disorder-averaged twist order parameters and spin correlations. We classify the types of random VBS phases depending on the strength of bond randomness $R$ and dimerization $D$ using the twist order parameter, which has a negative/positive sign for a VBS phase with odd/even $\sigma$. Our results demonstrate the existence of a multicritical point in the intermediate disorder regime with finite dimerization, where (2,2), (3,1), and (4,0) phases meet. This multicritical point is at the junction of three phase boundaries in the $R\text{−}D$ plane: the (2,2)-(3,1) and (3,1)-(4,0) boundaries that extend to zero randomness, and the (2,2)-(4,0) phase boundary that connects another multicritical point in the undimerized limit. The undimerized multicritical point separates a gapless Haldane phase and an infinite-randomness critical line with the diverging dynamic critical exponent in the large-$R$ limit at $D=0$. Furthermore, we identify the (3,1)-(4,0) phase boundary as an infinite-randomness critical line even at small $R$ and find the signature of infinite randomness at the (2,2)-(3,1) phase boundary only in the vicinity of the multicritical point.

The pursuit of elemental superconductors with high critical temperature $\left(T_c\right)$ holds immense scientific significance due to their relatively simple material composition, which not only aids in understanding the mechanisms of superconductivity but also facilitates further exploration of high-temperature superconductors in compound materials. Here, we report experimental findings of superconductivity for scandium (Sc) in the pressure range of 64 GPa to 261 GPa. As a result, the highest $T_c$ of 37.6 K observed in Sc-V phase at 243 GPa sets the current record among all elements. An estimate based on the Ginzburg-Landau model gives a zero-temperature upper critical magnetic field of 21.3 T with a coherence length of 39.3 Å. Current-voltage measurements show that the zero-temperature critical current and its density can reach 0.146 A and 70 A ${\mathrm{mm}}^{-2}$ of Sc-IV at 198 GPa, and 0.116 A and 50 A ${\mathrm{mm}}^{-2}$ of Sc-V at 250 GPa, respectively. Further theoretical calculations show that pressure-induced phonon softening plays a key role in increasing $T_c$ upon compression. Our current findings shed light on the understanding of high superconductivity in simple elements and provide insight into exploring high-$T_c$ elemental superconductors and Sc-based superconductors.

We study a chemically active binary mixture undergoing phase separation and show that under nonequilibrium conditions, stable liquid spherical shells can form via a spinodal instability in the droplet center. A single liquid shell tends to grow until it undergoes a shape instability beyond a critical size. In an active emulsion, many stable and stationary liquid shells can coexist. We discuss conditions under which liquid shells are stable and dominant as compared to regimes where droplets undergo shape instabilities and divide.

We study strong-field ionization of molecular hydrogen in highly intense corotating two-color laser fields. The measured electron momentum distributions show alternating half rings (AHRs) that are characteristic of subcycle interference. We report on the role of the two-color field ratio for the visibility of this subcycle interference. The ratio of the peak electric field at 780 nm compared to the peak electric field at 390 nm $E_{780}/E_{390}$ is varied from 0.037 to 0.18. We find very good agreement with the results from our semiclassical simulation. We conclude that the AHR pattern is visible if two conditions are fulfilled. First, the amplitudes of the two pathways that lead to the subcycle interference have to be similar, which is the case for low two-color field ratios $E_{780}/E_{390}$. Second, the phase difference of the two pathways must be strong enough to allow for destructive interference, which is the case for high two-color field ratios $E_{780}/E_{390}$. For typical experimental conditions, we find that two-color field ratios $E_{780}/E_{390}$ in the range from 0.037 to 0.12 lead to good visibility of the AHR pattern. This guides future experiments to measure the Wigner time delay using holographic angular streaking of electrons.