Combinatorial optimization problems are one of the areas where near-term noisy quantum computers may have practical advantage against classical computers. Recently, a feedback-based quantum optimization algorithm has been proposed by Magann et al., Phys. Rev. Lett. 129, 250502 (2022). The method explicitly determines quantum circuit parameters by feeding back measurement results thus avoids classical parameter optimization that is known to cause significant trouble in quantum approximate optimization algorithm, the well-studied near-term algorithm. Meanwhile, a significant drawback of the feedback-based quantum optimization is that it requires deep circuits, rendering the method unsuitable to noisy quantum devices. In this study we propose a feedback law for parameter determination by introducing the second-order approximation with respect to time interval, a hyperparameter in the feedback-based quantum optimization. This allows one to take larger time interval, leading to acceleration of convergence to solutions. In numerical simulations on the maximum cut problem we demonstrate that our proposal significantly reduces circuit depth, with its linear scaling with the problem size smaller by more than an order of magnitude. We expect that the feedback law proposed in this work may pave the way for feedback-based quantum optimization with near-term noisy quantum computers.

Stochastic resetting, a method for accelerating target search in random processes, often incurs temporal and energetic costs. For a diffusing particle, a lower bound exists for the energetic cost of reaching the target, which is attained at low resetting rates and equals the direct linear transportation cost against fluid drag. Here, we study smart resetting, a strategy that aims to beat this lower bound. By strategically resetting the particle only when this benefits its progress toward the target, smart resetting leverages information to minimize energy consumption. We analytically calculate the energetic cost per mean first passage time and show that smart resetting consistently reduces the energetic cost compared to regular resetting. Surprisingly, smart resting achieves the minimum energy cost previously established for regular resetting, irrespective of the resetting rate. Yet, it fails to reduce this cost further. We extend our findings in two ways: first, by examining nonlinear energetic cost functions, and second, by considering smart resetting of drift-diffusion processes.

Maxwell lattices are periodic frameworks characterized by a balance between the number of kinematic variables and constraints in each unit cell, attracting attention as a source of topological mechanical metamaterials. In particular, one-dimensional (1D) Maxwell lattices maintain a constant number of degrees of freedom (DOFs) as the number of unit cells increases, offering advantages in the design and control of their kinematics. Here, we construct a 1D Maxwell lattice with tunable DOFs, termed the Maxwell origami tube, by closing a triangulated origami tessellation, which is a 2D Maxwell lattice. In topological mechanics, the infinitesimal deformation modes of uniformly configured Maxwell lattices are classified into edge and bulk modes. Unlike conventional 1-DOF 1D Maxwell lattices, multi-DOF 1D Maxwell lattices exhibit a mixture of DOFs corresponding to edge and bulk modes, which we term eDOFs and bDOFs. This paper investigates how the eDOFs and bDOFs of the Maxwell origami tube depend on the crease patterns and folded states using a discrete dynamical system model. We find that ground states with zero and nonzero bDOFs can coexist within a single crease pattern. Additionally, in states with nonzero bDOF, the ratio of bDOF to total DOF decreases as the total DOF increases. In contrast, we present another origami tube with a constant DOF of 2, which represents the first overconstrained lattice to exhibit nonuniform bulk modes. This work highlights the versatility of Maxwell lattices and provides a theoretical foundation for designing novel mechanical metamaterials.

We introduce a method for improved loading into a single-atom optical tweezer array, utilizing iterative loading with multiple reservoir tweezers. Demonstrated with dual-wavelength tweezer arrays of ${}^{88}\mathrm{Sr}$ atoms, our approach achieves a $96\%$ loading rate after four reload cycles. This method can significantly enhance existing tweezer rearrangement protocols and reduce optical power consumption for target tweezer arrays, thereby enabling a larger number of atoms in a quantum logic device. Our approach has the potential for implementation with different atomic species and combination with other enhanced loading methods.

The advent of x-ray free electron lasers (XFELs) has opened the door to exploring the x-ray-nuclei interaction in the multiexcitation regime. Here, we show the dependence of emission properties of x-ray photons from an ensemble of identical resonant nuclei on the number of excitations in the system, employing the theoretical methods we have introduced. Under the multiexcitation condition, the detection of x-ray quanta is emission-order selective. Our work presents an efficient approach to investigating the multiphoton emission of x-rays from cooperative resonant nuclei within and beyond weak-excitation regimes, offering valuable insights into nonlinear x-ray optics and many-body nuclear physics.

Neural network decoders can achieve a lower logical error rate compared to conventional decoders, like minimum-weight perfect matching, when decoding the surface code. Furthermore, these decoders require no prior information about the physical error rates, making them highly adaptable. In this study, we investigate the performance of such a decoder using both simulated and experimental data obtained from a transmon-qubit processor, focusing on small-distance surface codes. We first show that the neural network typically outperforms the matching decoder due to better handling of errors leading to multiple correlated syndrome defects, such as $Y$ errors. When applied to the experimental data of Google Quantum AI [R. Acharya et al., Nature (London) 614, 676 (2023)], the neural network decoder achieves logical error rates approximately $25\%$ lower than minimum-weight perfect matching, approaching the performance of a maximum-likelihood decoder. To demonstrate the flexibility of this decoder, we incorporate the soft information available in the analog readout of transmon qubits and evaluate the performance of this decoder in simulation using a symmetric Gaussian-noise model. Considering the soft information leads to an approximately $10\%$ lower logical error rate, depending on the probability of a measurement error. The good logical performance, flexibility, and computational efficiency make neural network decoders well-suited for near-term demonstrations of quantum memories.

Microscopic models based on multilevel atoms are central to optimizing nonlinear optical responses and the coherent control of light. These models are traditionally based on single-atom effects that are parametrically extrapolated to include collective effects, such as an enhanced response or propagation within atomic media. In this work, we present a systematic analysis of the cooperative effects arising in driven systems composed of multilevel atoms coupled via a common electromagnetic environment. The analysis is based on an interplay between dressed states induced by the driving field and photon exchanges, and collective decay channels. This theory is applied to the case of four-wave mixing induced by a pair of lasers acting on an atomic pair with internal levels in the diamond configuration. The effect of interatomic correlations and collective decay over the photons created in this nonlinear process is then explored. We identify three regions of operation: (i) laser-dominated, (ii) intermediate, and (iii) dipole-dominated. The dependence of single- and two-photon correlations is studied in detail for each region by varying atomic orientations and laser parameters consistent with current experiments involving atomic gases. Photonic correlation functions are shown to exhibit a transition from a Lorentz-like dependence on the two-photon detuning—with general features that can be obtained in an isolated atom scheme—to a two-peaked distribution when the dipole-dipole interactions become relevant. For weak Rabi frequencies whose value is smaller than the highest collective decay rate, the atoms are trapped inside their ground state as they approach each other. It is found that the anisotropy of the dipole-dipole interaction and its wave nature are essential to understand the behavior of the photon correlations. Signatures of these processes are identified for existing experimental realizations. The intuition obtained from this connection helps to uncover relevant parameters that could be exploited for quantum control protocols based on dispersive and dissipative cooperative effects in multilevel systems.

We propose a scheme to detect the Unruh effect in a circularly rotated Unruh-DeWitt detector enclosed within a cylindrical cavity. This technique relies on the enhanced atomic spontaneous emission rate related to the counter-rotating coupling between the detector and massless scalar fields. Our analysis demonstrates that the integration of a cylindrical cavity, coherent light excitation, and multiatom superradiation significantly enhances the signal strength, as the radiation rate associated with the standard rotating-wave coupling can be greatly suppressed within the cavity. Compared to linear acceleration, circular motion can significantly reduce the atomic acceleration path length, leading to increased detection efficiency and lower experimental difficulty. Our method provides an avenue for exploring relativistic effects on a compact, tabletop platform.

Picocavities serve as exceptional platforms for extreme atomic-scale field localization, enabling opportunities for light-matter interactions at subnanometer scales. Here we introduce a scattering-theory-based mode analysis approach to model the interaction between picocavity and nanocavity modes, providing insights into picoscopic field-field dynamics. By employing coupled-mode theory, we specifically investigate the coupling between multiple nanocavity quasinormal modes (QNMs) and a single picocavity QNM, highlighting the development of spectral correlations through mode hybridization. Our findings reveal that the induced hybridization can be controlled by adjusting the picocavity geometry and the frequency spacing of the excited modes. This strong field-field coupling at picoscopic scales leads to spectral mixing, which alters the optical density of states, facilitates the identification of Fano resonances, and produces spectrally stable picocavity modes with modified mode-mixing characteristics. Furthermore, the coupled-mode framework enables the modeling of quantum dynamics resembling the Jaynes-Cummings model, describing the interaction between a hybridized two-level system and a quantized picocavity field. These insights into picoscopic light-matter interactions pave the way for advancements in subnanometer photonic quantum dynamics.

Using time- and angle-resolved photoemission spectroscopy, we examine the unoccupied electronic structure and electron dynamics of the type-I Weyl semimetal ${\mathrm{PtBi}}_2$. Using the ability to change the probe photon energy over a wide range, we identify the predicted Weyl points in the unoccupied three-dimensional band structure and we discuss the effect of $k_{\perp }$ broadening in the normally unoccupied states. We characterize the electron dynamics close to the Weyl points and in other parts of three-dimensional Brillouin zone using $k$-means, an unsupervised machine-learning technique. This reveals distinct differences—in particular, that the electron dynamics close to the Weyl points are slower than in Brillouin zone regions close to the bulk Fermi surface.

Recent research indicates that small-world networks can enhance weak signals through a phenomenon called topological resonance. However, the underlying mechanism of this effect remains unclear. In this study, we examine a ring of four bistable oscillators to analyze how it responds to an external subthreshold signal. Our findings reveal that the ring amplifies the signal via synchronization competition, where two desynchronized oscillators compete to synchronize with the other two, leading to increased oscillation and improved response to the subthreshold signal. Additionally, we find that synchronization competition is influenced by both the initial states of the oscillators and the characteristics of the subthreshold signal. We identify three synchronization transitions during signal amplification and propose three feedforward motifs to analyze the signal response of the ring. Finally, we apply the synchronization competition mechanism to explain the topological resonance of small-world networks.

Hyperbolic materials, characterized by their hyperbolic dispersion relations of waves, have attracted significant attention due to their distinct equifrequency contours. This study establishes a correlation between electron hyperbolicity and light hyperbolicity, unveiling fascinating scenarios resulting from their interplay. The transition of electron hyperbolicity from type-I to type-II, induced by varying the chemical potential of electrons, also triggers a similar transition in light hyperbolicity. Through first-principles calculations, $\alpha \text{−}{\mathrm{CaGe}}_2$ is identified as a promising material for achieving this transition. The tunable optical responses in $\alpha \text{−}{\mathrm{CaGe}}_2$ under different doping conditions allow for the creation of heterostructures, resulting in the emergence of unique optical phenomena such as negative refraction and negative reflection. The distinctive electronic states proposed in this study not only pave the way for further exploration of hyperbolic materials but also lay the groundwork for designing innovative optoelectronic devices that expand the functional possibilities of these intriguing materials.

We study the dynamic generation of persistent current by phase imprinting fermionic atoms in a ring geometry at zero temperature. Mediated by the pairing interaction, the Fermi condensate dynamically acquires a quantized current by developing azimuthal phase slips, as well as density and pairing-order-parameter depletions. Resorting to the Bogolioubov–de Gennes formalism, we investigate the time evolution of the transferred total angular momentum and the quantized superfluid current throughout the phase-imprinting process. This enables a detailed self-consistent analysis of the impact of interaction, as well as different initial pairing states, on the superflow formation, in contrast to previous theoretical analysis based on the Gross-Pitaevskii equation with artificially imposed phases. In particular, we show that, as the interaction strength increases, the azimuthal density distribution becomes less susceptible to the phase imprinting potential, leading to a smaller quantized current under the same imprinting parameters. Our results offer microscopic insights into the dynamic development of superflow in the phase-imprinting process, and are helpful for the ongoing experimental effort.

In this work, we extend the analysis of interacting bosons at 2D-1D dimensional crossover for finite size and temperature by using the field theory approach $\left(bosonization\right)$ and quantum Monte Carlo simulations. Stemming from the fact that finite size low-dimensional systems are allowed only to have quasiordered phase, we consider the self-consistent harmonic approximation to compute the fraction of quasicondensate and its scaling with the system size. It allows us to understand the important role played by finite size and temperature across the dimensional crossover for deciding the condensate nature. Furthermore, we consider a mean-field approximation to compute the finite size effect on the crossover temperature for both weak and strong interactions. All the physical quantities we discuss here provide essential information for quantum gas at dimensional crossover and are directly detectable in cold atom experiments.

Adaptation refers to the ability to recover and maintain “normal” function on perturbations of internal or external conditions and is essential for sustaining life. Biological adaptation mechanisms are dissipative, i.e., they require a supply of energy such as the coupling to the hydrolysis of ATP. Via evolution the underlying biochemical machinery of living organisms evolved into highly optimized states. However, in the case of adaptation processes two quantities are optimized simultaneously, the adaptation speed or accuracy and the thermodynamic cost. In such cases one typically faces a trade-off, where improving one quantity implies worsening the other. The solution is no longer unique but rather a Pareto set—the set of all physically attainable protocols along which no quantity can be improved without worsening another. Here we investigate Pareto fronts in adaptation-dissipation trade-offs for a cellular thermostat and a minimal ATP-driven receptor-ligand reaction network. We find convex sections of Pareto fronts to be interrupted by concave regions, implying the coexistence of distinct optimization mechanisms. We discuss the implications of such “compromise-optimal” solutions and argue that they may endow biological systems with a superior flexibility to evolve, resist, and adapt to different environments.