Hybrid variational quantum algorithms are promising for solving practical problems, such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. However, variational quantum algorithms (derived from randomized hardware-efficient ansatz or adaptive ansatz) become a black box, not trustworthy for model interpretation, and not to mention for application deployment in informing critical decisions. In this paper, we construct the first interpretable quantum regression algorithm, in which the quantum state exactly encodes the classical data table and the variational parameters correspond directly to the regression coefficients, which are real numbers by construction, providing a high degree of model interpretability and minimal cost to optimize due to the right expressiveness. We also exploit the encoded data structure to reduce the gate complexity of computing the regression map. To reduce circuit depth in nonlinear regression, our algorithm can be extended by directly constructing nonlinear features via classical preprocessing, such as independent encoded column vectors. By design, the model performance is determined by the cost function measurement results $\mathcal{C}$ synchronous to the mean squared errors (MSE) for the regression models. We derived the read-out errors induced by one-hot encoding and compact encoding; the required physical qubit resources are exponentially compressed for the compact encoding to be favorable for noisy quantum devices. We also derive the cost function dependent sample complexity $ \in \mathcal{O}\left(σ^{2}(\mathcal{C}) \ln (1/α)/ε^{2}\right)$ under the error budget $ε$ and confidence tolerance $α$.

We develop an incomplete-contracting model with accounting-based covenants to study how covenant accounting adjustments are made and what properties they exhibit. Standard accounting rules (e.g., GAAP) can generate false-alarm errors or undue-optimism errors. The manager can exert costly effort to privately identify these errors and propose adjustments. If errors are not corrected, control rights may be inefficiently allocated, leading to costly renegotiation. We show that (1) adjustments always correct false-alarm errors, but correct undue-optimism errors only when their magnitude is small; and (2) the manager may expend socially wasteful effort to identify these errors. The model yields testable empirical predictions and policy implications.

How to synthesize a dataset while achieving differential privacy for AI model training is a meaningful but challenging problem. To address this problem, state-of-the-art methods first select original private dataset's multiple low-dimensional distributions that have the potential to approximate the distribution of original private dataset with high precision, and then synthesize a dataset obeying all selected low-dimensional distributions as the synthetic dataset. However, it is difficult to select suitable low-dimensional distributions, which in turn degrades the data utility of resulting synthetic dataset. To improve differentially private dataset synthesis, we propose to train a differentially private AI model for downstream tasks on the original private dataset and utilize the trained model to synthesize datasets. In particular, on the one hand, the AI model satisfies differential privacy so no matter how to use the model does not disclose private information of original private dataset. On the other hand, the AI model is trained to complete the downstream task so the AI model preserves critical information for completing downstream tasks. We utilize the AI model to synthesize datasets to achieve the goal of improving data utility while preserving privacy. Empirical evaluations on four benchmark datasets demonstrate that our proposed DPDSyn consistently outperforms eight state-of-the-art baselines with a maximum improvement of 2.40x in accuracy and 333.73x in synthesis efficiency. Further experiments also validate that DPDSyn has strong scalability across varying data scales.

This paper considers the problem of reachability analysis of control systems with optimal controllers, as a first step towards verifying the safety and correctness of such systems. Despite their appeal in guaranteeing task satisfaction through cost minimization, optimal controllers are often challenging to assure. In particular, as system dynamics grow in complexity, solving the resulting optimization problem may be difficult, especially given time and computation constraints on real platforms. Thus, it is essential to verify that, even if the optimal solution is not always found, such controllers still accomplish the high-level control objective. In this paper, we focus on gradient descent algorithms and design a reachability algorithm by treating gradient descent as a separate (digital) dynamical system, embedded in the original (physical) dynamical system, with controls as part of the state. We evaluate the feasibility of the proposed method on two control systems, a two-dimensional quadrotor and a cartpole.

We present distances to 56 molecular clouds within $10\degr \leq l \leq 20\degr$ and $|b| \leq 5.25\degr$ from the Milky Way Imaging Scroll Painting (MWISP) $^{12}$CO survey, 47 of which are first-time determinations. The molecular clouds were identified using the DBSCAN algorithm, and their distances were measured with the model-calibrated color-distance method using $J-K{_s}$ colors and the distances provided by 2MASS and \textit{Gaia} EDR3. The distances range from $\sim$275 pc to $\sim$2118 pc. We also derived the physical properties of molecular clouds and found a moderate correlation between the dust extinction and the $^{12}$CO integrated intensity.

Coverage closure is the most time-consuming phase of hardware verification, and recent large language model (LLM)-based coding agents offer a promising approach to automated stimulus generation. However, prior LLM-based flows do not systematically analyze which coverage holes remain difficult to close or how inference-time computation is allocated during agentic verification. As a result, the efficiency limits and failure modes of LLM-based coverage closure remain poorly understood, particularly for large designs. We present an empirical study using a two-tier agentic framework comprising a base Codex agent and an enhanced domain-specialized LangGraph system. Our framework enables a taxonomy of coverage holes: methodology-bound ceilings (integration tied-off hardware, infeasible boundaries, dead code) and reasoning frontiers (protocol sequencing, multi-module pipeline warm-up, narrow timing conditions), exposing fundamental limits of purely LLM-driven approaches. We further instrument the system to track token usage across six categories, including system prompt, design comprehension, stimulus generation, coverage feedback, error recovery, and agentic overhead. We show that domain specialization shifts token allocation toward coverage-directed reasoning and improves efficiency. Across designs, the enhanced system achieves comparable or higher coverage (95-99%) while using 4-13x fewer tokens and converging to coverage targets 2-4x faster than a general-purpose baseline. Our results characterize the limits of LLM-based coverage closure, inform benchmark design and human escalation strategies, and guide profile-driven agent design for hardware verification.

For a simple graph $G$ with $n$ vertices, let $A_G$ denote the adjacency matrix of $G$, and let $λ_1(G) \geq λ_2(G) \geq \dots \geq λ_n(G)$ be its eigenvalues. For an integer $p \geq 2$, the positive $p$-energy and negative $p$-energy of $G$, denoted $\mathcal{E}^+_p(G)$ and $\mathcal{E}^-_p(G)$, are defined as follows: $\mathcal{E}^+_p(G) = \sum_{λ_i(G) > 0} |λ_i(G)|^p$ and $\mathcal{E}^-_p(G) = \sum_{λ_i(G) < 0} |λ_i(G)|^p,$ respectively. Tang, Liu, and Wang proposed a conjecture that, for any integer $p \geq 2$, every connected $n$-vertex graph $G$ satisfies $\mathcal{E}^+_p(G) \geq \mathcal{E}^+_p(P_n)$. Akbari, Kumar, Mohar, and Pragada conjectured that, for any $p \geq 2$, every connected $n$-vertex graph $G$ satisfies $\mathcal{E}^-_p(G) \geq \mathcal{E}^-_p(K_n)$, and they proved this conjecture for $p \geq 4$. In this paper, we prove that every connected $n$-vertex graph, except for $K_1$, $K_2$, and $P_3$, satisfies $\mathcal{E}^+_3(G) \geq \frac{\sqrt{5}}{2}n$. Moreover, we show that for any integer $p \geq 3$, every connected $n$-vertex graph $G$ satisfies $\mathcal{E}^-_p(G) \geq \mathcal{E}^-_p(K_n)$, which improves upon the previously known result.

This paper investigates the dynamics of closed vortex filaments in $\R^3$ governed by the Localized Induction Equation. Recently, Aiki and Higaki (2026) established the nonlinear orbital stability of circular vortex filaments under asymmetric perturbations, while identifying Lyapunov instability due to the linear growth of translation modes. Motivated by this result, we prove the existence of a family of closed solutions, which we call axial screw motions, that bifurcate from a circular filament. These solutions remain uniformly close to the orbit of the circle, but drift secularly away from the reference motion because their translation speed along the symmetry axis differs from that of the circular filament. In particular, they provide explicit non-trivial perturbations that satisfy orbital-stability estimates while failing Lyapunov stability, thereby realizing the gap between orbital stability and Lyapunov stability near the circular filament.

GaAs quantum dots grown by droplet etching epitaxy are high-quality solid-state sources of quantum light. Despite implementation in devices that exploit quantum phenomenon, a comprehensive review on the crystal growth of quantum dots grown by droplet etching epitaxy is absent, unlike for other quantum dot growth techniques such as the related droplet epitaxy method or Stranski-Krastanov growth of InAs quantum dots. This review presents a detailed overview of the droplet etching epitaxy growth technique in the molecular beam epitaxy environment, with emphasis on the growth parameters necessary to realize high-quality quantum dots. We systematically cover the three main phases of droplet etching epitaxy - droplet deposition, droplet etching, and nanohole regrowth - and relate experimental results to theories on crystal growth. The review concludes with an introduction to GaAs quantum dot photoluminescence and the extension of droplet etching epitaxy beyond the AlGaAs/GaAs material system.

It is proved that for a graph of order $n$, where $n\ge 6$, if the signless Laplacian index is larger than or equal to certain value depending on $n$, then the graph contains a trebly chorded cycle, where the chords incident to a common vertex, unless it is one of two specified graphs.

Although the relationship between molecular gas content and the star formation rate (SFR) has been extensively studied in nearby galaxies, it remains controversial whether the star formation efficiency (SFE) depends on galactic structure. In particular, whether the SFE is suppressed in the bar region compared with other structures, and the physical origin of this suppression, remain poorly understood. In this study, we investigate variations in the SFE and its physical drivers in the bar region of the nearby spiral galaxy M83, using multi-wavelength observations toward the bar and spiral arm regions on a scale of 200 pc observed with ALMA. From the molecular gas surface density derived from $^{12}$CO($J=2-1$) and $^{13}$CO($J=1-0$), the dense molecular gas surface density derived from HCN ($J = 1-0$), and the star formation rate surface density determined from extinction-corrected H$α$, we find that the SFEs in the bar region are roughly a factor of two lower than those in the spiral arm, indicating that the SFE is systematically suppressed in the bar. Moreover, we find that the SFEs of dense gas are lower in the bar than in the arm by a factor of about 0.35. These results suggest that not only the efficiency of converting bulk molecular gas into stars reduced in the bar, but the efficiency of star formation from dense gas is also lower. In addition, the CO line widths are systematically larger in the bar region and exhibit a negative correlation with both the SFE and the dense-gas SFE, consistent with the interpretation that enhanced turbulent motions hinder star formation. Although the analysis is limited to small regions of M83, our results suggest that the suppression of the SFE is related to large-scale dynamical effects on the molecular gas, such as strong shocks induced by cloud-cloud collisions and/or shear, both driven by non-circular motions in the bar.

Blocklisting is a common technique for preventing the use of known malicious content. However, conventional blocklisting infrastructures require either the blocklist to be public or clients to reveal their queries to the blocklist server. In this work, we introduce a private blocklisting framework, Half-Moon Cookie, by which a client can check an item against a proprietary blocklist held by a server, to determine whether the item is close to any blocklist element in a metric space. Critically, our design separates the embedding step from the blocklist check, so that performance degrades with their sum and not their product. Still, this check might be too costly to perform on the critical path of using the item, and so our design also supports a very efficient check that an item previously passed the blocklist check. In doing so, we support applications where one client can perform the blocklist check on the item before sending it, and recipients can more efficiently confirm the previous result before using the item, thereby avoiding TOCTOU attacks. We demonstrate how Half-Moon Cookie can be instantiated for similarity-based malware detection, enabling effective identification of malicious executables without revealing client inputs or disclosing the underlying blocklist.

Altermagnets (AMs) and fully compensated ferrimagnets (FC-FIMs) are emerging classes of magnetic materials that combine the advantages of antiferromagnets and ferromagnets. Here, we elucidate the mechanism behind the uniaxial strain-driven transformation from AM to FC-FIM and find that the accompanying non-relativistic valley polarization is positively correlated with the net magnetic moment between magnetic atoms in opposite spin sublattices. We then propose an uncompensated ferrimagnetic monolayer VCrSeTeO to achieve large intrinsic valley polarization. Spin-orbit coupling (SOC) is shown to further increase the valley polarization to over 400 meV under uniaxial strains and the reason is explained in terms of SOC perturbation theorem. Furthermore, we reveal a distinctive anomalous valley Hall effect in which the valley Hall voltage is reversed within the same valley in ferrimagnet VCrSeTeO. This work proposes a strategy for realizing giant valley polarization and provides theoretical guidance for the application of ferrimagnetic ferrovalley semiconductors derived from altermagnets in valleytronics.

Twisted graphene layers (TGLs) provide a powerful platform for investigating multiple quantum phenomena, yet their scalable deployment is hindered by the lack of reliable synthesis with precise angle. Here, benefited from a deeper understanding of the interplay between grain index and graphene growth kinetics, we report a scalable strategy to grow TGLs with pre-designed twist angles on platinum (Pt) via chemical vapor deposition (CVD), Through a combination of complementary in situ methods, we identified the activity sequence of different Pt grains and attributed it to the area ratio of exposed (110) facets during graphene-induced surface reconstruction. Moreover, we revealed that CVD-grown graphene orientation is determined by the grain-orientation-dependent surface morphology. By leveraging the so-established correlations between grain index with both graphene growth priority and its orientation, we achieve controlled folding and tearing of graphene overlayer using a pair of adjacent grains with dramatically different catalytical activity and kink-free atomic steps. We reveal that overlayer-induced step bunching and terrace reconfiguration critically govern the domain morphology and folding direction. Building on this mechanistic insight, we demonstrate a substrate-engineering framework where specific platinum grains are rationally selected to yield TGLs with pre-designed twist angles, including magic angle with flat band dispersion. This work not only highlights fundamental kinetics of Pt catalyzed graphene CVD growth, but also offers a generalizable methodology for manipulating foldable two-dimensional materials via dynamic substrate reconstruction, exampled by programmable growth of high-quality TGLs on open surfaces.

Apple Intelligence is a generative AI (GenAI) service provided by Apple on its devices. While offering a similar set of features as other similar GenAI services, Apple Intelligence is claimed to be designed with an extra focus on user security and privacy through a two-stage authentication and authorization design using anonymous access tokens. In this paper, we present our investigation into this token issuance mechanism with a goal to reveal possible vulnerabilities using traffic analysis, reverse engineering, and cross comparison with Apple's public documentation. Specifically, we present the Serpent attack, the first practical cross-device token replay attack against Apple Intelligence that allows the attacker to steal the access tokens from the victim's device and utilise them on a different device, with all usage rate-limited against the victim. We have achieved successful attacks on the latest macOS 26 Tahoe and demonstrated that an attacker, who even has used up its own allowance, can immediately regain access to Apple Intelligence service. We have responsibly disclosed the vulnerabilities to the vendors and received confirmation from Apple with CVE assigned and bounty given. Our results highlight a general lesson for built-in AI services: Anonymising identity does not by itself make the AI service secure; Enforcing non-transferability requires cryptographic binding to the rightful user.

In the conventional principal-agent problem, a principal delegates a task to an agent and formulates a contract to incentivize the agent's actions on behalf of the principal. However, this framework overlooks the information that is possibly available during the delegation process in some scenarios. To address this limitation, we propose a novel model that incorporates multiple intermediate states to capture such information revealed during the delegation. Furthermore, to evaluate the impact of the information embedded in these intermediate states, we introduce two distinct contracts: the pay-halfway contract, which provides payments based not only on final outcomes but also on intermediate states, and the terminate-halfway contract, which allows the principal to terminate the delegation process upon encountering undesirable intermediate states. This leads to the question of whether and how these contract types can leverage intermediate-state information? In particular, we ask: Can these contract types outperform standard contracts, and if so, when and to what extent? We answer the first question affirmatively and provide several important insights regarding the second, shedding light on the circumstances in which intermediate-state-aware contracts yield substantial advantages.

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn3Si2Te6, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.

The introduction of nonlinearities into lattices with topological band structures has led to the discovery of various types of solitons. The Su-Schrieffer-Heeger (SSH) lattice, as the most fundamental topological model, has been extended into the nonlinear regime. In particular, nonlinear edge states and bulk solitons exhibiting intensity humps against a zero background have been extensively studied in nonlinear SSH lattices. In this paper, we systematically investigate dark solitons in nonlinear SSH lattices. These dark solitons maintain a nonzero and constant background, featuring intensity dips either in the bulk of the lattice or at its edges, and residing spectrally in the semi-infinite gap or the middle finite gap. Regardless of the specific type of dark soliton, the intensity dip remains wellpreserved and is not affected by the band structure of the original linear lattice. Although the dark solitons we have identified are generally dynamically unstable across a broad range of parameters, several types exhibit linear stability when the intracell coupling is much larger than the intercell coupling. Our findings may provide valuable insights for the exploration of novel types of solitons in nonlinear topological lattices.

Backward stability is a desirable property for a well-designed numerical algorithm: given an input, a backward stable floating-point program produces the exact output for a nearby input. While automated tools for bounding the forward error of a numerical program are well-established, few existing tools target backward error analysis. We present a formal framework that enables sound, automated backward error analysis for a broad class of numerical programs. First, we propose a novel generalization of the definition of backward stability that is both compositional and flexible, satisfied by a wide range of floating-point operations. Second, based on this generalization, we develop the category Shel where morphisms model stable numerical programs, and show that structures in Shel support a rich variety of backward error analyses. Third, we implement a tool, eggshel, that automatically searches within a syntactic subcategory of Shel to prove backward stability for a given program. Our algorithm handles many programs with variable reuse, a known challenge in backward error analysis. We prove soundness of our algorithm and use our tool to synthesize backward error bounds for a suite of programs that were previously beyond the reach of automated analysis.

We introduce an ansatz for force-free electrodynamics in Minkowski spacetime under which the nonlinear system reduces to a semilinear scalar wave equation depending on two spacetime variables. This reduction yields explicit time-dependent solutions, including type-changing configurations with finite energy per unit transverse area and a null kink-type example. For traveling-wave solutions in the magnetically dominated regime, the kernel distribution of the field defines minimal field-sheet foliations.

Time-varying media, characterized by dynamic or spacetime-modulated constitutive parameters such as permittivity and permeability, have recently emerged as a transformative paradigm for advanced wave control, transcending the constraints imposed by temporal translation symmetry and energy conservation in static systems. By incorporating time as an active degree of freedom, such media unlock unique phenomena including broadband frequency conversion, temporal refraction, significant field enhancement, and magnet-free nonreciprocity. These capabilities are reshaping the landscape of photonic technologies, enabling groundbreaking applications such as broadband nonreciprocal amplifiers, non-resonant lasers, and highly efficient particle accelerators. This review systematically classifies time-varying media based on their modulation schemes and elucidates the underlying physical principles and distinctive wave-matter interactions. We comprehensively survey the latest advances in this rapidly evolving field, highlighting exotic wave behaviors and practical implementations across electromagnetic and photonic systems. Furthermore, we summarize experimental platforms that realize time-varying responses across different frequency regimes. Finally, we assess the current state of progress, identify key challenges, and offer a forward-looking perspective on future research directions in this dynamic and promising area.

We introduce a Hybrid Quantum Residual Network (HQRN) and establish an exact functional correspondence between its state evolution and the dynamics of classical networks with residual connections. When inputs are restricted to the computational basis, the HQRN reduces to its classical analog, enabling the direct translation of optimized classical weights into quantum unitary operations, effectively inheriting the landscape benefits of classical optimization. Conversely, when processing general mixed states, the HQRN leverages off-diagonal quantum correlations to resolve features inaccessible to its classical analog. We validate this framework through digit recognition and bipartite entanglement classification. Notably, HQRN achieves high classification accuracy even for adversarial separable states that mimic the marginal measurement statistics of entangled pairs. Our results bridge the gap between classical and quantum residual learning, paving a scalable pathway for deep quantum architectures.

Rules files (e.g., AGENTS.md, CLAUDE.md) are the primary mechanism for human-agent alignment when developers vibe code. However, they remain passive: it is not immediately apparent when rules are being used or followed, or how to improve them. To transform rules from passive text into active controls, we introduce ZORO, an interactive interface that integrates directly with a coding agent and anchors rules to every step of the coding process. After an agent generates an initial plan, ZORO enriches the plan with rules, enforces the rules during implementation by requiring the agent prove that each rule was followed, and allows users to provide in-situ feedback when they are unsatisfied with a rule application to evolve the ruleset. A technical evaluation shows that coding agents follow rules more with ZORO than without. A user study demonstrates a change in people's behavior and cognitive strategies when rules are at the forefront of vibe coding. We discuss how making rules active in agentic systems unlocks broader opportunities for human-agent alignment in coding settings and beyond.

We show that a common Lyapunov matrix exists for the convex combination of two Hurwitz matrices if and only if the intersection of the set of strict Lyapunov matrices for one matrix and the set of non-strict Lyapunov matrices for the other is nonempty. This simple relaxation is useful for the convergence analysis of the augmented primal-dual gradient flow for constrained optimization problems with affine inequality constraints, which can be viewed as a polytopic linear parameter-varying (LPV) system driven by the active-constraint selector. Under a relaxed strong convexity condition, exponential convergence is proved for the LPV system. The analysis can further be extended to the integral quadratic constraints (IQCs) framework for LPV systems to facilitate numerical search of the convergence rate.

Neuro-symbolic AI is gaining traction in domains such as large language models, scientific discovery, and autonomous systems due to its ability to combine perception with structured reasoning. However, its deployment is often constrained by high memory demands, diverse computation patterns, and complex hardware requirements. Existing hardware platforms struggle with large on-chip memory overheads, frequent pipeline stalls, limited I/O bandwidth, and inefficient handling of nonlinear operations. To address these key computational bottlenecks, we propose Overmind, a unified neuro-symbolic architecture with cross-layer optimizations. Overmind tackles these core bottlenecks through Padé approximations for universal nonlinear functions, preemptive memory bypass that eliminates costly on-chip caches, and a complete software stack that optimizes model deployment. By reconfiguring the Padé orders for approximating nonlinear functions, we also demonstrate adaptive accuracy-performance scaling. Overmind achieves an energy efficiency of 8.1 TOPS/W and a throughput of 410 GOPS for mixed neuro-symbolic workloads with minimal model accuracy loss. Compared to existing solutions, Overmind improves performance and efficiency with significantly fewer hardware resources.

Tuberculosis (TB) remains a leading global infectious disease, causing approximately 1.3 million deaths and 10.6 million new infections annually. Classical compartmental ODE models are the standard epidemiological tool for TB, yet their fixed-parameter structure cannot adapt to time-varying dynamics, unmodeled effects, or heterogeneous real-world data. This paper presents a methodological framework and proof-of-concept for applying Physics-Guided Neural Ordinary Differential Equations (PG-NODE) to TB transmission modeling within a SLIR (Susceptible, Latent, Infectious, Recovered) compartmental framework. We perform a rigorous mathematical analysis of the SLIR model, including derivation of the basic reproduction number $\mathcal{R}_0$, equilibrium analysis, and normalized sensitivity indices. We then reformulate the SLIR system as a PG-NODE, preserving compartmental conservation laws and biological constraints while enabling neural network components to learn unknown or time-varying rate functions from data. Three simulation scenarios illustrate the framework's intended capabilities: (i) adaptive tracking of time-varying transmission rates, (ii) correcting for unmodeled treatment and relapse dynamics with 27\% lower RMSE than the classical SLIR, and (iii) comparative forecasting of competing intervention policies over a 20-year horizon. Simulation results indicate that PG-NODE has strong potential for improving predictive accuracy while maintaining epidemiological interpretability; full adjoint-based training on real WHO surveillance data is identified as the key next step for empirical validation.

Recovering a source signal from indirect measurements often requires estimating latent parameters, such as wireless channel states or MRI coil sensitivities, that cannot be directly observed. Here, we introduce Physics-Embedded Inverse Learning (PEIL), in which a learned estimator predicts these parameters and a fixed, physics-based inverse operator uses them to reconstruct the signal, so that training requires only the source signal as supervision. In systems where multiple parameter combinations can reconstruct the signal equally well, the estimator exploits this freedom to coordinate parameters that compensate for residual modelling errors rather than match ground-truth parameters. In high-mobility wireless communications, PEIL discovers task-optimal configurations that outperform baselines given access to ground-truth parameters, enabling zero-shot generalisation and over 20-fold reduction in training data relative to supervised baselines. To test whether these properties extend across physical domains, we apply PEIL to parallel MRI, where it discovers physically interpretable coil sensitivity maps without calibration scans, yielding reconstructions grounded purely in acquired measurements. These results demonstrate that non-identifiability, conventionally a liability, becomes a resource when the learning objective targets reconstruction quality rather than parameter accuracy.

In this paper, we tackle the Multiple Watchman Route Problem (MWRP), which aims to find a set of paths that M watchmen can follow such that every location on the map can be seen by at least one watchman. First, we propose multiple methods to reduce the state space over which a search needs to be conducted by pruning map areas that are guaranteed to be seen en route to other areas. Next, we introduce MWRP-CP3, an efficient optimal planner that combines these methods with techniques that improve the quality and calculation time of existing heuristics. We present several suboptimal algorithms with bounds on solution quality, including MxWA*, a general variant of weighted A* for makespan problems. We also present anytime variations of our suboptimal algorithms, as well as techniques to improve an existing suboptimal solution by solving multiple decomposed sub-problems. We show that MWRP-CP3 can reduce the search space by more than 95% and runs more than 200x faster than existing optimal algorithms on 2D grid maps. We also show that our suboptimal algorithms solve maps 3x larger than those solvable by MWRP-CP3. See mwrp-cp3.github.io for the open source codebase and video demonstrations.

We carried out inelastic neutron scattering experiments on a powdered sample of the antiferromagnetic (AFM) uniform spin-1/2 chain compound CuSb2O6.The magnetic excitations appear in the energy range of 1.8 to 13 meV at 2.5 K below the AFM transition temperature (TN = 8.7 K).The gap value (1.8 meV) is close to that evaluated from the specific heat (1.51 meV). The excitations at 12.5 K (> TN) appear gapless. Thus, the 1.8 meV gap is caused by some anisotropy in spin-wave excitations. The gap excitations are strongest at 0.48 corresponding to a length of 0.66 nm. This result is consistent with the theoretical one that the interaction in a Cu pair with a length of 0.65562 nm (Jab) is strongest. The magnetic excitations can be explained by the AFM uniform XXZ chain with Jab = 6.437 meV and DJab = 0.063 meV. The 1.8 meV gap is caused by the small Ising anisotropy (DJab/Jab = 0.0098).

The controlled generation of nonclassical light beyond single photons remains a central challenge in quantum optics, due to the difficulty of enhancing multiphoton processes while suppressing lower-order excitations. Here we propose an interference-interaction-engineered scheme for programmable few-photon emission in a cavity-QED system of three atoms coupled to orthogonal cavity modes. By adiabatically eliminating an auxiliary Fabry-Pérot cavity, we generate a tunable cavity-mediated spin-exchange interaction $χ$, which, combined with a controllable geometric phase $ϕ$, reshapes the many-body dressed-state spectrum. This interplay enables selective addressing of excitation manifolds ($N=1,2,3$), establishing a direct mapping between excitation structure and photon-emission channels. For $ϕ=0$, constructive interference enhances the spectral anharmonicity of low-excitation manifolds, yielding tunable single- and two-photon emission associated with the $N=1$ and $N=2$ manifolds. In contrast, for $ϕ=2π/3$, destructive interference suppresses lower-order excitation pathways and activates a resonant three-photon channel originating from the $N=3$ manifold. Importantly, the cavity-mediated interaction $χ$ further enhances spectral separation between manifolds, enabling a substantial improvement in multiphoton purity while maintaining a sizable photon population. We demonstrate a three-order-of-magnitude enhancement in two-photon purity and more than two orders of magnitude improvement in three-photon emission. Our results establish a unified interference-interaction framework in which effective optical nonlinearities can be programmably engineered through phase and interaction, providing a scalable route toward high-purity multiphoton sources and programmable quantum photonic devices.