Recent developments in quantum technologies have enabled significant improvements in the precision of optical sensing systems. This work explores the integration of distributed quantum sensing with optical gyroscopes to improve the estimation accuracy of angular velocity. Utilizing bright two-mode squeezed states (bTMSS), which offer high photon numbers and strong bipartite quantum correlations, we propose a configuration that leverages continuous-variable entanglement across multiple spatially separated optical gyroscopes. Unlike traditional quantum sensing that enhances a single sensor, our approach focuses on estimating a global phase shift—corresponding to the average angular rotation—across distributed optical gyroscopes with quantum-enhanced sensitivity. We analyze the phase sensitivities of different bTMSS configurations, including M-mode entangled bTMSS and separable $M$-bTMSS, and evaluate their performance through the quantum Cramér-Rao bound. The analysis shows that, with $5\%$ photon loss in every channel in the system, the proposed scheme shows a sensitivity enhancement of $\sim 9.3\mathrm{dB}$ beyond the shot-noise limit, with an initial squeezing of $\sim 9.8\mathrm{dB}$. The present scheme has potential applications in quantum-enhanced inertial navigation and precision metrology within emerging quantum networks.

A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, overparametrized models exhibit counterintuitive behaviors, such as “double descent” in which models of increasing complexity exhibit decreasing generalization error. Other models may exhibit more complicated patterns of predictive error with multiple peaks and valleys. Neither double descent nor multiple descent phenomena are well explained by the bias-variance decomposition. We introduce a decomposition that we call the generalized aliasing decomposition (GAD) to explain the relationship between predictive performance and model complexity. The GAD decomposes the predictive error into three parts: (1) model insufficiency, which dominates when the number of parameters is much smaller than the number of data points, (2) data insufficiency, which dominates when the number of parameters is much greater than the number of data points, and (3) generalized aliasing, which dominates between these two extremes. We demonstrate the applicability of the GAD to diverse applications, including random feature models from machine learning, Fourier transforms from signal processing, solution methods for differential equations, and predictive formation enthalpy in materials discovery. Because key components of the generalized aliasing decomposition can be explicitly calculated from the relationship between model class and samples without seeing any data labels, it can answer questions related to experimental design and model selection before collecting data or performing experiments. We further demonstrate this approach on several examples and discuss implications for predictive modeling and data science.

The transmission of light through an ensemble of two-level emitters in a one-dimensional geometry is commonly described by one of two emblematic models of quantum electrodynamics (QED): the driven-dissipative Dicke model or the Maxwell-Bloch equations. Both exhibit distinct features of phase transitions and phase separations, depending on system parameters such as optical depth and external drive strength. Here, we explore the crossover between these models via a parent spin model from bidirectional waveguide QED, by varying positional disorder among emitters. Solving mean-field equations and employing a second-order cumulant expansion for the unidirectional model—equivalent to the Maxwell-Bloch equations—we study phase diagrams, the emitter's inversion, and transmission depending on optical depth, drive strength, and spatial disorder. We find in the thermodynamic limit the emergence of phase separation with a critical value that depends on the degree of spatial order but is independent of Doppler broadening effects. Even far from the thermodynamic limit, this critical value marks a special point in the emitter's correlation landscape of the unidirectional model and is also observed as a maximum in the magnitude of inelastically transmitted photons. We conclude that a large class of effective one-dimensional systems without tight control of the emitters spatial ordering can be effectively modeled using a unidirectional waveguide approach.

Exceptional points (EPs) in anti-parity-time (APT)-symmetric systems have attracted significant interest. While linear APT-symmetric systems exhibit structural similarities with nonlinear dissipative systems, such as mutually injection-locked lasers, the correspondence between exceptional points in linear non-Hermitian Hamiltonians and bifurcation phenomena in nonlinear lasing dynamics has remained unclear. We demonstrated that, in a two-cavity system with APT symmetry and gain saturation nonlinearity, an EP coincides with a bifurcation point of nonlinear equilibrium states, which appears exactly at the lasing threshold. Although the EP and the bifurcation point originate from fundamentally different physical concepts, the bifurcation point is observable and retains key EP characteristics even above the lasing threshold. Notably, the bifurcation point that originates from the linear EP also bridges linear and nonlinear dynamics of the system: It serves as an accessible transition point in the nonlinear dynamics between the limit cycle and synchronization regimes. Furthermore, we clarified that beat oscillation that conserves the energy difference, which is a unique dynamic in the weak-coupling regime of a linear APT system, evolves into a nonlinear limit cycle with equal amplitudes in the two cavities in the presence of gain saturation. Our findings establish a direct link between EP-induced bifurcation points and nonlinear dynamics, providing fundamental insights into non-Hermitian and nonlinear optical systems.

Valleytronics, as an alternative to traditional electronics or spintronics, is based on the encoding of quantum information in pseudospin valley quantum numbers, rather than in charge or spin states. A key ingredient is the (optical) manipulation of valley states before loss of coherence, which can be as fast as 100 fs. Previous works have shown the possibility of valley state manipulation using external fields. Here, we propose uniaxial strain as a more flexible and robust scheme to manipulate the valley state through the breaking of the crystal symmetry and the concomitant lifting of the degeneracy of the $1s$ exciton energy. Our theory is corroborated by state-of-the-art numerical simulations in monolayer hBN and shows the possibility to control valley pseudospin at the attosecond timescale.

We experimentally demonstrate that information replication by templated ligation of DNA strands inherits a kinetic proofreading mechanism and achieves significant error suppression through cascade replication. A simple simulation model derived from the experimental results shows that templated ligation has a significant advantage over replication by polymerization for error suppression of long strands. Specifically, longer chains show lower error rates, significantly distinct from the chain-growth polymerization where errors typically accumulate with chain length. This mechanism provides a plausible route for high-fidelity replication in prebiotic chemistry and illustrates how physical principles such as nonequilibrium kinetics and network architecture can drive reliable molecular information replication. The approach also offers strategies for error suppression in biotechnology.

Bismuth has been shown to be topological in its different allotropes and compounds, with one of the most notable examples being the Bi-Sb alloy, the first 3D topological insulator ever discovered. In this paper, we explore two-dimensional alloys of Bi and Sb, both crystalline and amorphous, to determine the critical concentrations that render the alloys topological. For the amorphous alloy, we determine the effect of structural disorder on its topological properties, remarkably observing a trivial to topological transition as disorder increases. The alloys are modeled using a Slater-Koster tight-binding model and the topological behavior is assessed through the entanglement spectrum together with artificial neural networks. Additionally, we perform electronic transport calculations with results compatible with those of the entanglement spectrum, which, furthermore, reveal an insulator to metal transition in the highly disordered regime.

We present a method, based on the Keldysh formalism, for deriving stochastic master equations that describe the non-Markovian dynamics of a quantum system coupled to a Gaussian environment. This approach yields a compact expression for the system's propagator, which we show to be equivalent to existing formulations, such as the stochastic von Neumann equation (SVNE). A key advantage of our method is its generality: It can be extended to describe any open-system evolution defined on a suitable ordering contour. As a result, we adapt it to derive generalized versions of the SVNE that account for initial system-environment correlations, as well as stochastic equations that incorporate information about the statistics of energy flows in the environment. The insights offered by our technique further allow us to examine the nature of the noise processes appearing in the SVNE. We prove that its solution can be expressed in terms of a single physical noise, without any loss of information. Finally, we propose a semiclassical scenario in which this noise can be interpreted as arising from an initial measurement process on the environment.

Ultrahigh nonreciprocal transmission has been achieved in a cavity-magnon system, which consists of two whispering gallery modes (WGMs) and a single magnon mode within a magnetic insulator yttrium iron garnet sphere. The nonreciprocal frequency shift induced by the Sagnac effect enables unidirectional transmission of an input field, while suppressing propagation in the opposite direction, thereby facilitating nonreciprocal optical transmission. Within experimentally accessible parameter regimes, the optical isolation ratio can exceed 40 dB, representing the highest isolation ratio reported to date. Furthermore, applying squeezing to the magnon mode further enhances this isolation performance. Additionally, the directionality of light isolation can be reversed simply by modifying the rotation of the WGM cavity. These findings offer promising prospects for developing high-performance, tunable, and compact optical nonreciprocal devices.

Narrowband optimal control is proposed as a protocol of optimal quantum sensing, providing maximum accuracy for given resources. The procedure is demonstrated on the IBM quantum computer to estimate the amplitude of the qubit Rabi frequency. It is based on optimizing the quantum Fisher information (QFI) when it becomes equal to the classical Fisher information. This scenario offers a practical quantum sensing because it only involves population transfer to the excited state as the target without dynamical phases, which would otherwise be difficult to control and measure. The protocol uses the single-shot-shaped pulse method and an inverse optimization, to design a pulse that results in the optimal QFI in terms of energy or equivalently operating time, referred to in short as local narrowband inverse optimization (NIO). Multiple $\pi$ pulses are shown to be particular cases of this protocol. An alternative semiglobal optimal NIO procedure producing a partially nonoscillatory profile is also demonstrated. Our approach also offers application to optimal atomic selective excitation in an array of atoms for quantum technology.

Efficient signal representation is essential for the functioning of living and artificial systems operating under resource constraints. A widely recognized framework for deriving such representations is the information bottleneck method, which yields the optimal strategy for encoding a random variable, such as the signal, in a way that preserves maximal information about a functionally relevant variable, subject to an explicit constraint on the amount of information encoded. While in its general formulation the information bottleneck method is a numerical scheme, it admits an analytical solution in an important special case where the variables involved are jointly Gaussian. In this setting, the solution predicts discrete transitions in the dimensionality of the optimal representation as the encoding capacity is increased. Although these signature transitions, along with other features of the optimal strategy, can be derived from a constrained optimization problem, a clear and intuitive understanding of their emergence is still lacking. In our work, we advance our understanding of the Gaussian information bottleneck method through multiple mutually enriching perspectives, including geometric and information-theoretic ones. These perspectives offer intuition about the set of optimal encoding directions, the nature of the critical points where the optimal number of encoding components changes, and the way the optimal strategy navigates between these critical points. We then apply our treatment of the information bottleneck to a previously studied signal prediction problem, obtaining insights into how different features of the signal are encoded across multiple components to enable optimal prediction of future signals. Altogether, our work deepens the foundational understanding of the information bottleneck method in the Gaussian setting, motivating the exploration of analogous perspectives in broader, non-Gaussian contexts.

At the nanoscale, random effects not only govern the dynamics of a physical system but may also affect its observation. This work introduces a paradigm for coarse graining that eschews the assignment of a unique coarse-grained trajectory to a microscopic one. Instead, observations are not only coarse-grained but also accompanied by a small chance of error. Formulating the problem in terms of path weights, we identify a condition on the structure of errors that ensures that the observed entropy production does not increase. As a result, the framework of stochastic thermodynamics for estimating entropy production can be extended to this broader class of systems. As an application, we consider Markov networks in which individual transitions can be observed but may be mistaken for each other. We motivate, derive, and illustrate thermodynamic bounds that relate the error sensitivity of the observed entropy production to the strength of the driving and are valid for arbitrary network topologies. If sufficiently many transitions in the network can be observed, redundancies in the coarse-grained trajectories can be used to detect and correct errors, which potentially improves naive estimates of entropy production. We conclude with an outlook on subsequent research on thermodynamic bounds for erroneous coarse graining.

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (nonictal) brain dynamics remain poorly understood. To investigate this, we analyzed an in-house dataset of brain activity recordings from epileptic zebrafish, focusing on two controlled genetic conditions across two fishlines. After using machine learning to segment and label recordings, we applied time-delay embedding and persistent homology—a noise-robust method from topological data analysis (TDA)—to uncover topological patterns in brain activity. We find that ictal and nonictal periods can be distinguished based on the topology of their dynamics, independent of genetic condition or fishline, which validates our approach. Remarkably, within a single wild-type fishline, we identified topological differences in nonictal periods between seizure-prone and seizure-free individuals. These findings suggest that epilepsy leaves detectable topological signatures in brain dynamics even outside of ictal periods. Overall, this study demonstrates the utility of TDA as a quantitative framework to screen for topological markers of epileptic susceptibility, with potential applications across species.

Partition function zeros are powerful tools in understanding critical behavior. In this paper, we present results of the Fisher zeros of two-dimensional Ising models, in the framework of free-fermion eight-vertex model. First, we succeed in finding special boundary conditions for the free-fermion model, under which the partition function of a finite lattice can be expressed in a double product form. Using appropriate mappings, these boundary conditions are transformed into the corresponding versions of the square, triangular, and honeycomb lattice Ising models. Each Ising model is studied in the cases of a zero field and of an imaginary field $i(\pi /2)k_{B}T$. For the square lattice model, we rediscover the famous Brascamp-Kunz (B-K) boundary conditions. For the triangular and honeycomb lattice models, we obtain the B-K-type boundary conditions, and the Fisher zeros are conveniently solved from the product form of partition function. The advantage of B-K-type boundary conditions is that the Fisher zeros of any finite lattice exactly lie on certain loci, and the accumulation points of zeros can be easily determined in the thermodynamic limit. Our finding and method would be very helpful in studying the partition function zeros of vertex and Ising models.

The quantum switch has been widely studied as a prototypical example of indefinite causal order in quantum information processing. However, the potential advantages of utilizing more general forms of indefinite causal orders remain largely unexplored. We study higher-order switches, which involve concatenated applications of the quantum switch, and we demonstrate that they provide a strict advantage over the conventional quantum switch in the task of quantum channel distillation. Specifically, we show that higher-order quantum switches enable the probabilistic distillation of any qubit Pauli channel into the identity channel with nonzero probability. This capability contrasts with the conventional quantum switch, which allows only a limited set of Pauli channels to be distilled with nonzero probability. We observe that, counterintuitively, the distillation rate generally increases the noisier the channel is. We fully characterize the asymptotic distillation rates of higher-order superswitches for qubit Pauli channels. Finally, we prove a no-go result for multiqubit generalizations.

Fault-tolerant quantum computers promise the simulation of complex quantum systems beyond the reach of classical computation. In contrast, current noisy intermediate-scale quantum devices are constrained by hardware noise. Consequently, quantum simulation methods remain limited in their near-term applicability. Two prominent techniques addressing these challenges are zero-noise extrapolation (ZNE) and incremental structural learning (ISL). In this work, ZNE and ISL are benchmarked for simulating the Trotterized time evolution of two models: the Tavis-Cummings model (TCM) and the Heisenberg spin chain (HSC), using a classically simulated noisy hardware backend. The methods are evaluated on the basis of the accuracy of expectation values relative to noiseless simulations and their resource demands such as circuit depths and shot counts. The impact of noise on optimization routines in ISL, previously underexplored, is also investigated. Results indicate that ISL performs more favorably in HSC systems, consistently surpassing ZNE in expectation value accuracy. Conversely, for the TCM, ISL generally yields lower accuracies despite reduced Trotter circuit depths, with weak interactions often leading to pronounced phase lags or flat expectation curves. Notably, when performing ISL optimization under noiseless conditions, the protocol is generally able to reduce dephasing errors, but average accuracies still vary on the simulated Hamiltonian. Our findings highlight the sensitivity of quantum simulation protocols to the structure of the Hamiltonian encoding system dynamics. Trends across systems suggest that ISL optimization benefits from Trotter circuits with stronger interactions, and that ansatz construction favors isotropic couplings. Moreover, although ISL introduces approximation errors, it demonstrates greater robustness than ZNE in systems with deeper Trotter circuits.

Variational quantum algorithms (VQAs) have attracted remarkable interest over the past few years because of their potential computational advantages on near-term quantum devices. They leverage a hybrid approach that integrates classical and quantum computing resources to solve high-dimensional problems that are challenging for classical approaches alone. In the training process of variational circuits, constructing an accurate probability distribution for each epoch is not always necessary, creating opportunities to reduce computational costs through shot reduction. However, existing shot allocation methods that capitalize on this potential often lack adaptive feedback or are tied to specific classical optimizers, which limits their applicability to common VQAs and broader optimization techniques. Our observations indicate that the information entropy of a quantum circuit’s output distribution exhibits an approximately exponential relationship with the number of shots needed to achieve a target Hellinger distance. In this work, we propose a distribution-adaptive dynamic shot (DDS) framework that efficiently adjusts the number of shots per iteration in VQAs using the entropy distribution from the prior training epoch. Our results demonstrate that the DDS framework sustains inference accuracy while achieving an $\sim 50\%$ reduction in average shot count compared to fixed-shot training, and $\sim 60\%$ higher accuracy than recently proposed tiered shot allocation methods. Furthermore, in noisy simulations that reflect the error rates of actual IBM quantum systems, DDS achieves an $\sim 30\%$ reduction in the total number of shots compared to the fixed-shot method with minimal degradation in accuracy and offers $\sim 70\%$ higher computational accuracy than tiered shot allocation methods.

We demonstrate a direct writing method for the fabrication of planar Josephson junctions from high-quality superconducting niobium titanium nitride (NbTiN) thin films, by creating local disorder using focused He ion beam irradiation. We control the suppression of superconductivity in NbTiN as a function of the helium ion beam fluence, enabling us to successfully fabricate Josephson junctions with highly tunable weak links ranging from the metallic to the insulating phase, utilizing the continuous nature of the disorder-induced superconductor-insulator transition. We demonstrate the successful fabrication of both superconductor-normal metal-superconductor (SNS) and superconductor-insulator-superconductor (SIS) type of devices, and show that we can achieve an exceptionally wide range ($\sim 5$ orders of magnitude) of critical current densities and junction resistances. The SNS-type junctions follow closely the ideal resistively and capacitively shunted junction behavior, have high characteristic voltages up to $\sim 1.5\mathrm{mV}$, and show Shapiro steps up to very high orders, while the SIS junctions also follow established theories well. The results suggest that the junctions are suitable for a wide range of applications in superconducting electronics because of the excellent mechanical, electrical, and microwave properties of NbTiN. In particular, as NbTiN has previously been used to fabricate high-quality factor microwave resonators, we see the method as a promising and simple way to realize high-frequency superconducting qubits and other quantum devices using only a single superconducting film.