Based on tensor-network realizations of path integrals reducing exponential memory scaling to polynomial efficiency and a Liouville space implementation of a time-discrete quantum memory, we investigate a quantum system simultaneously exposed to two structured reservoirs. For this purpose, we employ a numerically exact quasi-two-dimensional tensor network combining both diagonal and off-diagonal system-reservoir interactions with a twofold memory for continuous and discrete retardation effects. As a possible example, we study the non-Markovian dynamical interplay between discrete photonic feedback and structured acoustic phonon modes, resulting in emerging inter-reservoir correlations and long-living population trapping within an initially excited two-level system.

Josephson junctions are the basis for the most sensitive magnetic flux detectors, the definition of the unit volt by the Josephson voltage standard, and superconducting digital and quantum computing. They result from the coupling of two coherent quantum states, as they occur in superconductors, superfluids, atomic Bose Einstein Condensates (BECs), and exciton-polariton condensates. In their ground state, Josephson junctions are characterized by an intrinsic phase jump. Controlling this phase jump is fundamental for applications in computing. Here, we experimentally demonstrate controllable phase relations between photon BECs resulting from particle exchange in a thermo-optically tunable potential landscape. Our experiment realizes an optical analog of a controllable 0,$\pi$-Josephson junction. By connecting several junctions, we can study a reconfigurable 4-condensate system demonstrating the potential of our approach for analog spin-glass simulation. More generally, the combination of static and dynamic nanostructuring techniques introduced in our work offers a powerful platform for the implementation of adaptive optical systems for paraxial light in and outside of thermal equilibrium.

We theoretically study the lattice distortion induced first- and second-order topological phase transition in rectangular ${\mathrm{FeSe}}_x{\mathrm{Te}}_{1-x}$ monolayer. When compressing the lattice constant in one direction, our first-principles calculation shows that the ${\mathrm{FeSe}}_x{\mathrm{Te}}_{1-x}$ undergoes a band inversion at $\mathrm{\Gamma }$ point in a wide doping range, say $x\in (0.0,0.7)$, which indicates the first-order topological phase transition of its electronic band structure. Meanwhile, because the unidirectional pressure breaks ${\mathrm{C}}_4$ symmetry, the topological edge states along the (100) and (010) directions have different Dirac energy. Given the high-temperature superconductivity of ${\mathrm{FeSe}}_x{\mathrm{Te}}_{1-x}$ monolayer, we found that the ${\mathrm{C}}_4$ symmetry breaking is essential to support Majorana corner states in either presence or absence of time-reversal symmetry. Especially in the case of breaking time-reversal symmetry, we can obtain a single Majorana zero mode at each corner without concerning the details of the superconducting pairing symmetries and applied Zeeman form, which can potentially bring advantages in the experimental implementation.

Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials applications. Currently, advances in this area are hindered by the prohibitive cost of the quantum dynamics simulations needed to explore the principles and possibilities of molecular control. However, the emergence of nascent quantum-computing devices suggests that efficient simulations of quantum dynamics may be on the horizon. In this article, we study how quantum computers could be employed to design optimally-shaped fields to control molecular systems. We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in polynomial time, in combination with a classical optimization approach for updating the field. Qubit encoding methods relevant for molecular control problems are described, and procedures for simulating the quantum dynamics and obtaining the simulation results are discussed. Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems, and also consider how optimally-shaped fields could be used to elucidate the mechanisms of energy transfer in light-harvesting complexes. Resource estimates, as well as a numerical assessment of the impact of hardware noise and the prospects of near-term hardware implementations, are provided for the latter task.

Practical, useful computations are instantiated via physical processes. Information must be stored and updated within a system's configurations, whose energetics determine a computation's cost. To describe thermodynamic and biological information processing, a growing body of results embraces rate equations as the underlying mechanics of computation. Strictly applying these continuous-time stochastic Markov dynamics, however, precludes a universe of natural computing. Within this framework, operations as simple as a NOT gate (flipping a bit) and swapping two bits, and swapping bits are inaccessible. We show that expanding the toolset to continuous-time hidden Markov dynamics substantially removes the constraints, by allowing information to be stored in a system's latent states. We demonstrate this by simulating computations that are impossible to implement without hidden states. We design and analyze a thermodynamically costless bit flip, providing a counterexample to rate-equation modeling. We generalize this to a costless Fredkin gate—a key operation in reversible computing that is Turing complete (computation universal). Going beyond rate-equation dynamics is not only possible but also necessary if stochastic thermodynamics is to become part of the paradigm for physical information processing.

We explore the feasibility of optically forming long-range tetratomic and larger polyatomic molecules in their ground electronic state from ultracold pairs of polar molecules aligned by external fields. Depending on the relative orientation of the interacting diatomic molecules, we find that a tetratomic can be formed either as a weakly bound complex in a very extended halo state or as a pure long-range molecule composed of collinear or nearly collinear diatomic molecules. The latter is a novel type of tetratomic molecule comprised of two diatomic molecules bound at a long intermolecular range and predicted to be stable in cold and ultracold regimes. Our numerical studies were conducted for ultracold KRb and RbCs, resulting in the production of ${\left(\mathrm{KRb}\right)}_2$ and ${\left(\mathrm{RbCs}\right)}_2$ complexes, respectively. Based on universal properties of long-range interactions between polar molecules, we identify triatomic and tetratomic linear polar molecules with a favorable ratio of dipole and quadrupole moments for which the approach could be generalized to form larger polyatomic molecules.

We show that the resistivity plateau of ${\mathrm{SmB}}_6$ at low temperature, typically taken as a hallmark of its conducting surface state, can systematically be influenced by different surface treatments. We investigate the effect of inflicting an increasing number of handmade scratches and microscopically defined focused ion beam-cut trenches on the surfaces of flux-grown ${\mathrm{Sm}}_{1-x}{\mathrm{Gd}}_x{\mathrm{B}}_6$ with $x=0$ and 0.0002. Both treatments increase the resistance of the low-temperature plateau, whereas the bulk resistance at higher temperatures largely remains unaffected. Notably, the temperature at which the resistance deviates from the thermally activated behavior decreases with cumulative surface damage. These features are more pronounced for the focused ion beam treated samples, with the difference likely being related to the absence of microscopic defects such as subsurface cracks. Therefore, our method presents a systematic way of controlling the surface conductance.

Link failures in supply networks can have catastrophic consequences that can lead to a complete collapse of the network. Strategies to prevent failure spreading are thus heavily sought after. Here, we make use of a spanning tree formulation of link failures in linear flow networks to analyze topological structures that prevent failure spreading. In particular, we exploit a result obtained for resistor networks based on the matrix tree theorem to analyze failure spreading after link failures in power grids. Using a spanning tree formulation of link failures, we analyze three strategies based on the network topology that allow us to reduce the impact of single link failures. All our strategies either do not reduce the grid's ability to transport flow or do in fact improve it—in contrast to traditional containment strategies based on lowering network connectivity. Our results also explain why certain connectivity features completely suppress any failure spreading as reported in recent publications.

Memory effects in time series of experimental observables are ubiquitous, have important consequences for the interpretation of kinetic data, and may even affect the function of biomolecular nanomachines such as enzymes. Here we propose a set of complementary methods for quantifying conclusively the magnitude and duration of memory in a time series of a reaction coordinate. The toolbox is general, robust, easy to use, and does not rely on any underlying microscopic model. As a proof of concept we apply it to the analysis of memory in the dynamics of the end-to-end distance of the analytically solvable Rouse-polymer model, an experimental time series of extensions of a single DNA hairpin measured by optical tweezers, and the fraction of native contacts in a small protein probed by atomistic molecular dynamics simulations.

We theoretically investigate the elusive Andreev-Bashkin collisionless drag for a two-component one-dimensional Bose-Hubbard model on a ring. By means of tensor network algorithms, we calculate the superfluid stiffness matrix as a function of intra- and interspecies interactions and of the lattice filling. We then focus on the most promising region close to the so-called pair-superfluid phase, where we observe that the drag can become comparable with the total superfluid density. We elucidate the importance of the drag in determining the long-range behavior of the correlation functions and the spin speed of sound. In this way, we are able to provide an expression for the spin Luttinger parameter $K_S$ in terms of drag and the spin susceptibility. Our results are promising in view of implementing the system by using ultracold Bose mixtures trapped in deep optical lattices, where the size of the sample is of the same order of the number of particles we simulate. Importantly, the mesoscopicity of the system, far from being detrimental, appears to favor a large drag, avoiding the Berezinskii-Kosterlitz-Thouless jump at the transition to the pair-superfluid phase which would reduce the region where a large drag can be observed.

We present a hydrodynamic theory to describe a chiral electron system with a Weyl spin-orbit interaction on a field-theoretic basis. Evaluating the momentum flux density as a linear response to a driving electric field, we derive an equation of motion for the orbital angular momentum. It is shown that the chiral nature leads to a dynamic bulk angular momentum generation by inducing a global torque as a response to the applied field. The steady-state angular momentum is calculated taking into account rotational viscosity.

Three-dimensional phase-contrast imaging of multiply-scattering samples in x-ray and electron microscopy is challenging due to small numerical apertures, the unavailability of wave front shaping optics, and the highly nonlinear inversion required from intensity-only measurements. In this work, we present an algorithm using the scattering matrix formalism to solve the scattering from a noncrystalline medium from scanning diffraction measurements and simultaneously recover the illumination aberrations. We demonstrate our method experimentally in a scanning transmission electron microscope, recovering the scattering matrix of a heterogeneous sample with two layers of multiwall carbon nanotubes filled with ${\mathrm{TaTe}}_2$ core-shell structures, spaced $10\mathrm{nm}$ apart in the axial direction. Our work enables phase contrast imaging and materials characterization in multiply-scattering samples at high resolution for a wide range of materials.

We propose and investigate the intrinsically thinnest transistor concept: a monolayer ballistic heterojunction bipolar transistor based on a lateral heterostructure of transition metal dichalcogenides. The device is intrinsically thinner than a field effect transistor because it does not need a top or bottom gate, since transport is controlled by the electrochemical potential of the base electrode. As is typical of bipolar transistors, the collector current undergoes a tenfold increase for each 60 mV increase of the base voltage over several orders of magnitude at room temperature, without sophisticated optimization of the electrostatics. We present a detailed investigation based on self-consistent simulations of electrostatics and quantum transport for both electrons and holes of a $p\text{−}n\text{−}p$ device using ${\mathrm{MoS}}_2$ for the 10-nm base and ${\mathrm{WSe}}_2$ for the emitter and collector. Our three-terminal device simulations confirm the working principle and a large current modulation $I_{\text{ON}}/I_{\text{OFF}}\sim {10}^8$ for $\mathrm{\Delta }{V}_{\mathrm{EB}}=0.5\mathrm{V}$. Assuming ballistic transport, we are able to achieve a current gain $\beta \sim {10}^4$ over several orders of magnitude of collector current and a cutoff frequency up to the THz range. The exploration of the rich world of bipolar nanoscale device concepts in two-dimensional materials is promising for their potential applications in electronics and optoelectronics.

We propose an integrable spin-1 chain with a magnetic impurity whose spin is $1/2$. The integrability of the model is based on an operator solution of the associated reflection equation. Due to the existence of the impurity, the SU(3) symmetry of the bulk is broken. By using the nested algebraic Bethe ansatz, we obtain the exact solution of the system. The eigenvalues, eigenstates, and Bethe ansatz equations are given explicitly. The method provided in this paper can be generalized to other high-rank quantum integrable systems with magnetic impurities where the spins of the bulk and of impurities are different.

The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest in Hamiltonian neural networks (HNNs) with physical constraints defined by Hamilton's equations of motion, which represent a major class of physics-enhanced neural networks. We introduce a class of HNNs capable of adaptable prediction of nonlinear physical systems: by training the neural network based on time series from a small number of bifurcation-parameter values of the target Hamiltonian system, the HNN can predict the dynamical states at other parameter values, where the network has not been exposed to any information about the system at these parameter values. The architecture of the HNN differs from the previous ones in that we incorporate an input parameter channel, rendering the HNN parameter-cognizant. We demonstrate, using paradigmatic Hamiltonian systems, that training the HNN using time series from as few as four parameter values bestows the neural machine with the ability to predict the state of the target system in an entire parameter interval. Utilizing the ensemble maximum Lyapunov exponent and the alignment index as indicators, we show that our parameter-cognizant HNN can successfully predict the route of transition to chaos. Physics-enhanced machine learning is a forefront area of research, and our adaptable HNNs provide an approach to understanding machine learning with broad applications.

This paper presents a mathematical and numerical analysis of the flat-band wave functions occurring in the chiral model of twisted bilayer graphene at the “magic” twist angles. We show that the chiral model possesses an exact intravalley inversion symmetry. Writing the flat-band wave function as a product of a lowest Landau level quantum Hall state and a spinor, we show that the components of the spinor are antiquantum Hall wave functions related by the inversion symmetry operation introduced here. We then show numerically that as one moves from the lowest to higher magic angles, the spinor components of the wave function exhibit an increasing number of zeros, resembling the changes in the quantum Hall wave function as the Landau level index is increased. The wave function zeros are characterized by a chirality, with zeros of the same chirality clustering near the center of the moiré unit cell, while opposite chirality zeros are pushed to the boundaries of the unit cell. The enhanced phase winding at higher magic angles suggests an increased circulating current. Physical implications for scanning tunneling spectroscopy, orbital magnetization and interaction effects are discussed.

Point defects in crystalline materials often occur in multiple charge states. Although many experimental methods to study and explore point defects are available, techniques to explore the nonequilibrium dynamics of the charge states of these defects at ultrafast (subnanosecond) timescales are rare. We present results from ultrafast optical-pump supercontinuum-probe spectroscopy measurements on $\beta \text{−}{\mathrm{Ga}}_2{\mathrm{O}}_3$. The probe absorption spectra shows defect absorption peaks at two energies, $\sim 2.20\mathrm{eV}$ and $\sim 1.63\mathrm{eV}$, within the 1.3–2.5 eV probe energy bandwidth. The strength of the absorption associated with each peak is time dependent and the spectral weight shifts from the lower energy peak to the higher energy peak with pump-probe delay. Further, maximum defect absorption is seen for probes polarized along the crystal $c$ axis. The time-dependent probe absorption spectra and the observed dynamics for all probe wavelengths at all pump-probe delays can be fit with a set of rate equations for a single multilevel defect. Based on first-principles calculations within hybrid density-functional theory, we attribute the observed absorption features to optical transitions from the valence band to different charge states of Gallium vacancies. Our results demonstrate that broadband ultrafast supercontinuum spectroscopy can be a useful tool to explore charge states of defects and defect dynamics in semiconductors.

We study self-gravitating bosonic systems, candidates for dark-matter halos, by carrying out a suite of direct numerical simulations designed to investigate the formation of finite-temperature, compact objects in the three-dimensional (3D) Fourier-truncated Gross-Pitaevskii-Poisson equation (GPPE). This truncation allows us to explore the collapse and fluctuations of compact objects, which form at both zero temperature and finite temperature. We show that the statistically steady state of the GPPE, in the large-time limit and for the system sizes we study, can also be obtained efficiently by tuning the temperature in an auxiliary stochastic Ginzburg-Landau-Poisson equation. We show that, over a wide range of model parameters, this system undergoes a thermally driven first-order transition from a collapsed, compact, Bose-Einstein condensate to a tenuous Bose gas (that is not gravitationally condensed). By a suitable choice of initial conditions in the GPPE, we also obtain a binary condensate that comprises a pair of collapsed objects rotating around their center of mass.

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask: Can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as worst-case perturbations by unknown noise sources. We show that by taking advantage of depolarization noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy, which can be extended to quantum differential privacy. For the protection of quantum data, this quantum protocol can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of the classification model are absent, thus also providing a potential quantum advantage for classical data. This opens the opportunity to explore other ways in which quantum noise can be used in our favor, as well as identifying other ways quantum algorithms can be helpful in a way which is distinct from quantum speedups.

We demonstrate the direct quantum nondemolition detection of a millihertz linewidth optical atomic transition. We observe the modification of the phase and amplitude of a probe field interacting with strontium atoms, which provides a direct spectroscopic signal to which a laser could be frequency stabilized. To investigate this measurement capability, we demonstrate an approach to determining the intrinsic natural lifetime of exceptionally long-lived optical excited states. Such transitions are key to the performance of state-of-the-art atomic clocks, have potential applications in searches for fundamental physics and gravitational wave detectors, as well as quantum many-body phenomena. Here, we determine the ratio of the challenging to measure and poorly known ultranarrow linewidth transition (${}^{3}P_0$ to ${}^{1}S_0$ in ${}^{87}\mathrm{Sr}$) to that of another narrow well-known transition (${}^{3}P_1$ to ${}^{1}S_0$) by coupling the two transitions to a single optical cavity and performing interleaved nondestructive measurements of the interaction strengths of the atoms with cavity modes near each transition frequency. We use this approach to determine the natural linewidth of the clock transition ${}^{3}P_0$ to ${}^{1}S_0$ in ${}^{87}\mathrm{Sr}$ to be ${\gamma }_0/\left(2\pi \right)=1.35\left(3\right)$ mHz or $\tau =118\left(3\right)$ s. The 30-$\mu \mathrm{Hz}$ resolution implies that we could detect states with lifetimes just below 2 h, and with straightforward future improvements, we could detect states with lifetimes up to 15 h, using measurement trials that last only a few hundred milliseconds, eliminating the need for long storage times in optical potentials. This work opens the path to nondestructive direct spectroscopy of ultranarrow transition for continuous frequency measurements and laser stabilization.