Single-molecule localization based on the concept of MINFLUX allows for molecular resolution imaging and tracking with a laser-scanning microscopy architecture. In MINFLUX, a doughnut-shaped structured illumination beam sequentially excites the sample, thereby minimizing the localization uncertainty for a given number of photons. However, MINFLUX has a limited field of view (FOV) and therefore requires a precise prelocalization step. We mitigate this problem by introducing the single-molecule image scanning microscopy (smISM) concept. In smISM, the point detector of the laser-scanning microscope is replaced with a small single-photon detector array, thereby leveraging the benefits of both structured illumination and structured detection. We show via simulations that by combining smISM and MINFLUX (i.e., ISM-FLUX), we obtained a localization uncertainty between 1 and 15 nm over a FOV of more than 800 nm with 100 photons. In addition, smISM allows the localization of multiple molecules simultaneously. We calculate the effect of different parameters, such as the relative position of the doughnut beams, the number of detector pixels, the number of photons, and the signal-to-background ratio, on the localization uncertainty. We predict that the combination of a good localization precision and the enhanced robustness of ISM-FLUX will help the wide adoption of MINFLUX.

Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.

Topological insulators feature a number of topologically protected boundary modes linked to the value of their bulk invariant. While in one-dimensional systems the boundary modes are zero dimensional and localized, in two-dimensional topological insulators the boundary modes are chiral, one-dimensional propagating modes along the edges of the system. Thus, topological photonic insulators with large Chern numbers naturally display a topologically protected multimode waveguide at their edges. Here, we show how to take advantage of these topologically protected propagating modes by interfacing them with quantum emitters. In particular, using a Harper-Hofstadter lattice, we find situations in which the emitters feature quasiquantized decay rates due to the increasing number of edge modes, and where their spontaneous emission spatially separates in different modes. We also show how using a single $\pi$ pulse the combination of such spatial separation and the interacting character of the emitters leads to the formation of a single-photon time-bin entangled state with no classical analog, which we characterize computing its entanglement entropy. Finally, we also show how the emitters can selectively interact with the different channels using nonlocal light-matter couplings such as the ones that can be obtained with giant atoms. Such capabilities pave the way for generating quantum gates among topologically protected photons as well as generating more complex entangled states of light in topological channels.

An all-optical method of ultrafast spin rotation is put forward to precisely manipulate the polarization of relativistic charged particle beams of leptons or ions. Laser-driven dense ultrashort beams are manipulated via single-shot interaction with a copropagating temporally asymmetric laser pulse of moderate intensity. Using semiclassical numerical simulations, we show that the initial polarization of a lepton (proton) beam can be rotated to any desired orientation by a flexible control over the phase retardation between the spin precession and the momentum oscillation in a temporally asymmetrical (e.g., half-cycle terahertz or frequency-chirped) laser field. In particular, spin ultrafast rotation from the common transverse to the more valuable longitudinal polarization is feasible in a picosecond scale or less. Moreover, the beam quality, in terms of energy and angular divergence, is improved in the rotation process. This method has potential applications in various areas demanding ultrafast spin manipulation, like laser-plasma, laser-nuclear, and high-energy physics.

Recently, gate-defined Josephson junctions in magic angle twisted bilayer graphene (MATBG) were studied experimentally, and highly unconventional Fraunhofer patterns were observed. In this work, we show that an interaction-driven valley-polarized state connecting two superconducting regions of MATBG would give rise to a long-sought-after purely electric controlled ${\phi }_0$-junction in which the two superconductors acquire a finite phase difference ${\phi }_0$ in the ground state. We point out that the emergence of the ${\phi }_0$-junction stems from the valley-polarized state which breaks time-reversal symmetry, and trigonal warping effects which break intravalley inversion symmetry. Importantly, a spatially nonuniform valley-polarization order parameter at the junction can explain the key features of the observed unconventional Fraunhofer patterns. Our work explores the novel transport properties of the valley-polarized state, and we suggest that gate-defined MATBG Josephson junctions could realize the first purely electric controlled ${\phi }_0$-junctions.

Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.

We generalize the classical shadow tomography scheme to a broad class of finite-depth or finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where the unitary ensemble is invariant under local-basis transformations. In this case, the reconstruction map for the classical shadow tomography depends only on the average entanglement feature of classical snapshots. We provide an unbiased estimator of the quantum state as a linear combination of reduced classical snapshots in all subsystems, where the combination coefficients are solely determined by the entanglement feature. We also bound the number of experimental measurements required for the tomography scheme, so-called sample complexity, by formulating the operator shadow norm in the entanglement feature formalism. We numerically demonstrate our approach for finite-depth local unitary circuits and finite-time local-Hamiltonian generated evolutions. The shallow-circuit measurement can achieve a lower tomography complexity compared to the existing method based on Pauli or Clifford measurements. Our approach is also applicable to approximately locally scrambled unitary ensembles with a controllable bias that vanishes quickly. Surprisingly, we find a single instance of time-dependent local Hamiltonian evolution is sufficient to perform an approximate tomography as we numerically demonstrate it using a paradigmatic spin chain Hamiltonian modeled after trapped ion or Rydberg atom quantum simulators. Our approach significantly broadens the application of classical shadow tomography on near-term quantum devices.

The electron-phonon $\left(e\text{−}\mathrm{ph}\right)$ coupling system often has a large number of phonon degrees of freedom, whose spectral functions are numerically difficult to compute using matrix product state (MPS) formalisms. To solve this problem, we propose a practical method that combines the Chebyshev MPS and the pseudosite density matrix renormalization group (DMRG) algorithm. The Chebyshev vector is represented by a pseudosite MPS with global $U\left(1\right)$ fermion symmetry, which maps $2^{N_p}$ bosonic degrees of freedom onto $N_p$ pseudosites, each with two states. This approach can handle arbitrary $e$-ph coupling Hamiltonians where pseudosite DMRG performs efficiently. We use this method to study the spectral functions of the doped extended Hubbard-Holstein model in a regime of strong Coulomb repulsion. Key features of the excitation spectra are captured at a modest computational cost. Our results show that weak extended $e$-ph couplings can increase the spectral weight of the holon-folding branch at low phonon frequencies, in agreement with angle-resolved photoemission observations on one-dimensional cuprates.

Variational quantum algorithms (VQAs) are expected to become a practical application of near-term noisy quantum computers. Although the effect of the noise crucially determines whether a VQA works, the heuristic nature of VQAs makes it difficult to establish analytic theories. Analytic estimations of the impact of the noise are urgent for searching for quantum advantages, as numerical simulations of noisy quantum computers on classical computers are heavy and quite limited to small-scale problems. In this paper, we establish analytic estimations of the error in the cost function of VQAs due to the noise. The estimations are applicable to any typical VQAs under Gaussian noise, which is equivalent to a class of stochastic noise models. Notably, depolarizing noise is included in this model. As a result, we obtain estimations of the noise level to guarantee a required precision. Our formulas show how the Hessian of the cost function, the spectrum of the target operator, and the geometry of the ansatz affect the sensitivity to the noise. This insight implies trade-off relations between the trainability and the noise resilience of the cost function. We also obtain rough estimations which can be easily calculated without detailed information of the cost function. As a highlight of the applications of the formula, we propose a quantum error mitigation method which is different from the extrapolation and the probabilistic error cancellation.

Both non-neural and neural biological systems can learn. So rather than focusing on purely brain-like learning, efforts are underway to study learning in physical systems. Such efforts include equilibrium propagation (EP) and coupled learning (CL), which require storage of two different states—the free state and the perturbed state—during the learning process to retain information about gradients. Here, we propose a learning algorithm rooted in chemical signaling that does not require storage of two different states. Rather, the output error information is encoded in a chemical signal that diffuses into the network in a similar way as the activation/feedforward signal. The steady-state feedback chemical concentration, along with the activation signal, stores the required gradient information locally. We apply our algorithm using a physical, linear flow network and test it using the Iris data set with 93% accuracy. We also prove that our algorithm performs gradient descent. Finally, in addition to comparing our algorithm directly with EP and CL, we address the biological plausibility of the algorithm.

In deep learning, neural networks serve as noisy channels between input data and its latent representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission and representation. While considerable efforts are concentrated on realizing optimal channel properties during network optimization, we study a frequently overlooked possibility that neural networks can be initialized toward optimal channels. Our theory, consistent with experimental validation, identifies primary mechanics underlying this unknown possibility and suggests intrinsic connections between statistical physics and deep learning. Unlike the conventional theories that characterize neural networks applying the classic mean-field approximation, we offer analytic proof that this extensively applied simplification scheme is not appropriate in studying neural networks as information channels. To fill this gap, we develop a restricted mean-field framework applicable for characterizing the limiting behaviors of information propagation in neural networks without strong assumptions on inputs. Based on it, we propose an analytic theory to prove that mutual information maximization is realized between inputs and propagated signals when neural networks are initialized at dynamic isometry, a case where information transmits via norm-preserving mappings. These theoretical predictions are validated by experiments on real neural networks, suggesting the robustness of our theory against finite-size effects. Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning. Our work may lay a cornerstone for promoting deep learning in terms of network initialization and suggest general statistical physics mechanisms underlying diverse deep learning techniques.

We report continuous Raman sideband cooling of a long ion chain close to the motional ground state beyond the Lamb-Dicke (LD) regime. By driving multiple sideband transitions simultaneously, we show that nearly all axial modes of a 24-ion chain are cooled to the ground state, with an LD parameter as large as $\eta =1.3$, spanning a frequency bandwidth of 4 MHz. Compared to traditional ground-state cooling methods such as pulsed sideband cooling or electromagnetically-induced-transparency cooling, our method offers two key advantages: disposal of pulse optimization, and a wide bandwidth covering the entire spectrum. This technique contributes as a crucial step for large-scale quantum information processing with linear ion chains and higher dimensions alike, and can be readily generalized to other atomic and molecular systems.

Transition metal dichalcogenides (TMDs) are a class of two-dimensional (2D) materials that have been widely studied for emerging electronic properties. In this paper, we use computational simulations to examine the water adsorption on TMDs systematically and the wetting property of tungsten diselenide ) specifically. We start with density functional theory (DFT) based random phase approximation (RPA), assessing the performance of exchange-correlation functionals and comparing water adsorption on various TMDs. We also perform ab initio molecular dynamics (AIMD) simulations on ${\mathrm{WSe}}_2$, from which we find that the distribution of interfacial water is sensitive to the exchange-correlation functional selected and a reasonable choice leads to a diffusive contact layer where water molecules prefer the “flat” configuration. Classical molecular dynamics (MD) simulations of water droplets on surfaces using appropriately parameterized water-surface interaction further confirm the dependence of water contact angle on the interaction and the interfacial water structure reproduced by different DFT functionals. Our study highlights the sensitivity of wetting to the water-substrate interaction and provides a starting point for a more accurate theoretical investigation of water-TMD interfaces.

To observe synchronization in large networks of classical or quantum systems demands both excellent control of the interactions between nodes and very accurate preparation of initial conditions due to the involved nonlinearities and dissipation. This limits its applicability for future devices. We demonstrate a route toward significantly enhancing the robustness of synchronized behavior in open nonlinear systems that utilizes the power of topology. In nontrivial topological lattices of quantum van der Pol oscillators, boundary synchronization emerges in the classical mean field as well as the quantum regime. In addition to its robustness against disorder and initial state perturbations, the observed dynamics is independent of the underlying topological model provided the existence of topological zero-energy modes. Our work extends the notion of topology to the general nonlinear dynamics and open quantum system realm with applications to networks where specific nodes need special protection like power grids or quantum networks.

Recently, the evolution of the Weyl point (WP) caused by the introduction of nonhermiticity into Weyl semimetals has aroused great research interest. We consider elastic flexural wave propagation in a phononic beam containing piezoelectric materials and introduce nonhermiticity through active regulation of external circuits. Considering a synthetic parameter space constituted by the one-dimensional Bloch wave vector and two geometrical parameters, we demonstrate that a double WP (DWP) arises at the band crossing. Then we study its evolution from the hermitic to nonhermitic situation under the effect of the active piezoelectric materials. We find that the DWP in the hermitic case evolves into a Weyl degenerate line and a Weyl hollow ring as concerns the real and imaginary parts of the Weyl frequencies, respectively. The formation mechanisms of the DWPs, lines, and rings are explained through the Hamiltonian of the system. Further, we observe the changes of the DWP and degenerate line in the transmission spectra of finite structures. Finally, we discuss the synthetic Fermi arc interface states through the analysis of the reflected phase vortices. In this paper, we provide insights into the high-dimensional Hermitian and non-Hermitian physics in elastic wave systems using synthetic dimensions.