Papers

Let $(M,g)$ be a smooth orientable $2d$ Riemannian manifold of genus $\mathfrak{g}$ with Riemannian metric $g$ and connected boundary $\Gamma$. Let $\Lambda$ be the Dirichlet-to-Neumann map on $\Gamma$ and let ${\rm det}_\zeta(\Lambda)$ be its (modified, i. e. with zero mode excluded) $\zeta$-regularized determinant. It is well-known that the quantity ${\rm det}_\zeta(\Lambda)/|\Gamma|$ (where $|\Gamma|$ is the length of $\Gamma$) is a conformal invariant. It was shown by Edward and Wu (\cite{EV}) that this invariant equals one for $\mathfrak{g}=0$; in the case $\mathfrak{g}>0$ Guillarmou and Guillopé \cite{Guillarmou} found two explicit expressions for this invariant through the Rouelle and (respectively) the Selberg zeta-functions of the two surfaces of negative constant curvature from the conformal class of $(M,g)$: one is of infinite volume and complete whereas another has geodesic boundary. We present an elementary counterpart of the formulae of Guillarmou and Guillopé using the periods of holomorphic differentials on the double $2M$ of $M$ only. Our approach is based on the properties of the Hilbert transform of $M$ \cite{B,HilbKor} and the Kontsevich-Vishik-Friedlander-Guillemin regularization of the determinants of pseudodifferenial operators \cite{KV,Ww,F}. In particular, a connection between the length spectra of (uniformized) $M$, $2M$ and the periods of holomorphic differentials on $2M$ is established.

Quantum computing is a promising way to study problems that are difficult for classical methods, but current quantum hardware still faces limits in scale, noise, and fidelity. Running quantum algorithms on physical machines can also be costly. Quantum circuit simulators therefore remain important because they let researchers design and test algorithms on classical computers before using quantum hardware. Most high-performance simulators provide GPU backends, while few offer native support for NPUs. This gap limits the computing platforms available for quantum-algorithm research. We developed Aicir to provide a full-stack quantum circuit simulator with a native Huawei Ascend NPU backend. Aicir connects circuit construction, several state representations, measurement, differentiation, variational algorithms, quantum machine learning, and quantum architecture search through one programming model. It also supports noise simulation, tensor-network and matrix-product-state engines, and distributed state simulation. On the NPU, paired real tensors, fixed-rank gate views, and hardware-specific formulas keep the tested simulation paths on the device. The same representation lets Aicir partition a state across $2^{p}$ NPUs while retaining reverse-mode differentiation. We validated native execution with CPU fallback disabled and checked distributed communication and gradients on 2, 4, and 8 NPUs. For the tested fused layered circuits, Aicir's CPU runtime is within $0.97$–$1.28\times$ that of Qiskit Aer and $0.76$–$1.10\times$ that of Cirq. These results place its CPU execution in the same range as established simulators for this workload, while the NPU tests establish correct native execution rather than CPU-to-NPU speedup.

Fault-tolerant quantum computation architectures are frequently bottlenecked by the overhead of producing high-fidelity magic states. In this work, we use algebraic geometric techniques to construct codes over binary extension fields $\mathbb{F}_{2^s}$, thus discovering new protocols for the distillation of qubit magic states, where our focus is on the regime of practical qubit-based quantum computing architectures. To do this, we show that multi-qubit gates of interest such as $\text{CS}$, $\text{CCZ}$, and $\text{TOF}\# = \text{CCZ}_{123}\text{CCZ}_{345}$, can be packaged into simple gates over the larger fields, and we derive simple algebraic conditions in the extension fields allowing the distillation of these gates. Because they are derived from Galois qudits, the corresponding qubits codes naturally handle the correlated errors present on such multi-qubit states. Moreover, the protocols we discover are extremely compact; for example, we show that 4 $\text{CS}$ states can be distilled to 1 $\text{CS}$ state at distance 2, using only 4 logical qubits. For a case study, we consider the distillation of $\text{CS}$ and $\text{CCZ}$ states from injected $\text{T}$ and $\text{CS}$ states. When optimized for magic state production per unit time, or logical spacetime volume, we find that our protocols outperform the state-of-the-art in almost every situation, both at input error rates $10^{-3}$ (direct injection), and $10^{-6}$ (allowing some cultivation pre-injection).

In the recently proposed scenario, the cosmological bounce occurs because the initially contracting Universe is not empty. In the region close to singularity, matter contents of the Universe heat up and effectively become radiation. Then, the trace anomaly automatically provides bounce if the overall beta function in the matter sector is positive. Independent of the remaining open questions on the quantum field theory side, it is interesting to consider this model from the cosmological perspective. In the present work, we develop the general formalism which is a necessary step for exploring the primordial cosmological perturbations. The main technical development is the formulation of the low-energy version for the nonlocal part of the effective action. The complete form of this action can be done local using two auxiliary scalars. In our new version, there are more scalars, but this enables one to avoid higher derivatives.

Charged colloids coated with a polymer brush can be designed to preferentially self-assemble into different crystal structures by varying easy-to-tune experimental conditions. For a given set of conditions, we have observed in experiments and simulations a distribution of thermodynamically (meta)stable self-assembled crystal structures. Properly quantifying the free energy landscape of these colloidal systems is essential for rationally choosing conditions to preferentially target particular crystal structures. For some of the structures we have formed, standard crystalline order parameters are not able to differentiate between crystals or between crystals and amorphous aggregates. We show that local environment similarity descriptors are able to distinguish the relevant metastable states, but are too expensive for use in biased MD simulations. Here, we adopt an approach from machine-learned interaction potentials showing that SE(3)-equivariant transformer networks can serve as an efficient-to-evaluate machine-learned proxy. As a result, we can compute the relative free energies of accessible colloidal structures as a function of different experimentally-relevant physical knobs that can steer our system between two observed crystal types. As an example application, we then show how changing surface potentials of positive and negative colloids while maintaining the same attractive energy can shift which crystal structure is favored.

Kurvature, a recently identified curvature invariant, has been argued to acquire superhorizon, or large-scale, white noise from hard-hard momentum coupling even when matter nonlinearities are small. If kurvature were related directly to cosmological curvature perturbations R through a Poisson equation, this white noise would cause an infrared-divergent variance sensitive to ultraviolet hard-mode physics. However, this relation does not generically hold. Kurvature is not intrinsic 3-curvature: on comoving slices it contains extrinsic-curvature terms, and intrinsic curvature is not related to R by a Poisson equation beyond linear order. We test this inference with second-order perturbation theory in radiation domination, relevant to CMB observables. Quadratic hard-hard composites do generate large-scale white noise in the kurvature density, but the Hamiltonian constraint separates it into intrinsic curvature and extrinsic shear, or equivalently density and expansion. Only the extrinsic terms carry the growing dimensionless kurvature density that mimics an ordinary density fluctuation above the horizon. The direct hard-hard curvature power is ultraviolet convergent, dominated by horizon-scale modes at evaluation, and leaves no IR relic in R from purely ultraviolet modes. By contrast, the Poisson construction of a curvature potential from kurvature is infrared divergent and cutoff sensitive; its white noise arises from extrinsic curvature associated with nonlinear acoustic beat modes in a radiation fluid.

We study the emergence of complexity in finite-size quantum systems as their dynamics transition from closed and coherent evolution to interacting and effectively open behavior. Using a state-based geometric framework, we represent mixed quantum states as probability measures on complex projective Hilbert space. This representation allows us to track how interactions reshape the underlying pure-state geometry. We introduce two complementary diagnostics: a distinguishability measure, based on the Wasserstein distance between probability-measure representations of mixed states, that quantifies sensitivity to initial states, and a state-space coverage index that measures long-time exploration of the subsystem state space. These diagnostics provide a geometric perspective on the emergence and evolution of quantum dynamical complexity. When applied to the quantum kicked top, both diagnostics generally increase with interaction strength. Their dependence on environment size is structured by parity symmetry, with integer-spin systems often exhibiting greater sensitivity and state-space coverage than half-integer-spin systems. These results highlight finite-size quantum effects and provide a geometric approach to quantifying dynamical complexity deep in the quantum regime

We conducted a comprehensive multi-wavelength analysis of a high energetic long-duration ZTF20abbiixp / GRB~200524A detected by Fermi Gamma Ray Burst Monitor (GBM). Our study combines extended high-energy observations from multiple space-based observatories including Fermi with broadband afterglow data spanning X-ray to radio wavelengths, complemented by extensive photometric and spectroscopic follow-up from several ground-based optical facilities worldwide like 3.6-m Devasthal Optical Telescope (DOT). ZTF20abbiixp / GRB~200524A exhibits almost negligible spectral lag, likely arising from the presence of multiple overlapping emission episodes, a property uncommon among long-duration bursts. The burst additionally shows a clear intensity-tracking evolution of the prompt-emission spectral parameters. The broadband afterglow light curve best fits with a broken powerlaw with a break at $10^{5}$ s since the GBM trigger. The electron powerlaw index (p) calculated from the temporal and spectral slopes fail to distinguish between a interstellar medium and a wind environment. Our custom-developed afterglow model fits the panchromatic data well, combining forward shock (FS) and reverse shock (RS) emission. The RS contribution required to fit the early time optical data. The inferred afterglow model parameters suggest that ZTF20abbiixp / GRB~200524A is a high energetic burst expanding into a dense ISM environment, with a relatively large value of the fraction of energy going to accelerating electron and magnetic field ($\epsilon_B$).

Compact spatial topology restricts the eigenmodes of primordial tensor perturbations, and the resulting discreteness can render the primordial stochastic gravitational-wave background (SGWB) anisotropic. Here we treat the CMB tensor $B$-mode covariance as a transfer-filtered measurement of that ultra-low-frequency anisotropy. Writing the normalized angular tensor-power measure as $F(k,\hat k)=1+Q(k,\hat k)$ and its nonmonopole moments as $q_{LM}(k)$, we obtain an explicit kernel that maps $q_{LM}(k)$ onto the off-diagonal covariance $\delta C^{BB}_{\ell m,\ell' m'}$. The kernel factorizes into tensor transfer functions and a spin-weighted Gaunt coefficient and obeys the parity rule $L+\ell+\ell'$ even for $BB$ and odd for $TB/EB$. It is an exact source–response representation of the full compact covariance rather than an additional observable. For a cubic three-torus the geometry pins down a common cubic angular subspace and orientation across frequency bands, although the amplitudes of the allowed multipoles still depend on the radial shell and source spectrum. The same topology-restricted template can therefore be read out either through the CMB $B$-mode kernel or through the anisotropy response of PTA/LISA/Taiji/TianQin searches. Using CAMB transfer functions and an invariant anisotropic-template statistic, we contrast this tensor channel with the scalar $T/E$ covariance. Independent direct angular-shell sums and $q_{LM}$–Gaunt contractions agree at $L_q^{\max}=2\ell_{\max}$ to relative Frobenius residuals of $1.4\times10^{-14}$–$3.0\times10^{-14}$. The scalar sector holds most of the practical CMB topology information; a fixed-template scan places the combined full-sky $S/N=1$ transition between $L/\chi_*=2.34$ and $2.36$, while the $B$-mode channel remains subthreshold but isolates the primordial SGWB contribution.

We investigate the dynamics of entanglement in a generalized version of the kicked Ising chain, extending the model from the standard qubit case (local dimension $q=2$) to higher local dimensions ($q > 2$). We identify the existence of ”dual-unitary” points where the model's space-time duality allows for exact analytical solutions. Our analysis reveals that while a few unique dual-unitary points exist analytically for systems with local dimensions $q=3$ and $q=4$, such points do not exist for $q \ge 5$ due to the lack of a unique kicking strength that satisfies the required matrix element conditions. Utilizing the transfer matrix method and a replica trick specifically adapted for higher dimensions, we derive exact expressions for the growth of entanglement entropy in the $q=3$ (kicked Potts-type) model starting from a class of solvable initial states. Our results demonstrate that at the dual-unitary point, both Rényi and von Neumann entanglement entropies grow linearly with time until reaching a maximum value determined by the subsystem size.

We investigate the role of symmetry-protected dark states and metastable-like frozen states in the autonomous charging dynamics of open quantum batteries described by a transverse-field Ising model. By comparing local and collective dissipation over a range of system sizes, temperatures, and magnetic phases, we demonstrate that collective dissipation generates symmetry-protected dark states together with a much larger set of frozen (metastable) states, forming an extended protected Hilbert space. We derive the multiplicity of the collective dark sector analytically, showing that it follows the Catalan sequence for even system sizes, while such states are absent for odd sizes. Our results show that collective dissipation can enhance ergotropy and charging power, with its advantage depending on temperature, magnetic phase, and system size. While the number of dark and frozen states is identical in the ferromagnetic and antiferromagnetic phases, the achievable ergotropy differs substantially because of the different spectral locations of these protected states. In particular, the antiferromagnetic configuration exhibits considerably larger extractable work owing to the favorable positioning of the protected subspaces within the many-body energy spectrum. Finally, we analyze the active Hilbert-space fraction and show that metastable protection provides an effective mechanism for suppressing dissipative losses while preserving efficient charging pathways. These results establish the dark-state and frozen-state sectors as key resources for optimizing the performance of open quantum batteries through engineered dissipation.

The $\sigma t$-$J$ model, obtained from the usual $t$-$J$ model by flipping the sign of the spin-down hopping term, has been proposed to eliminate the strong interference between doped holes and the spin background, known as the phase string effect. In this work, we investigate the finite-temperature properties of the $\sigma t$-$J$ model using infinite projected entangled-pair state (iPEPS) algorithms, in comparison with the $t$-$J$ model and its easy-plane variants. We show that these models share similar spin-pseudogap thermodynamics, with a maximum in the spin susceptibility at $T^* \sim J$ and a broad specific-heat peak. Tensor network renormalization (TNR) further provides strong evidences for a Berezinskii-Kosterlitz-Thouless (BKT) transition in the spin sector of the $\sigma t$-$J$ model at a lower temperature $T_\text{BKT} < T^*$ due to its reduced $\mathrm{U}(1)$ spin-rotation symmetry. Crucially, in contrast to the $t$-$J$ model and to its easy-plane variants that also exhibit BKT transitions, these signatures remain robust upon doping: the doped holes preserve and even enhance the antiferromagnetic correlations in the $xy$ spin plane, leading to a weak doping dependence of both $T^*$ and $T_\text{BKT}$. Finally, we develop a slave-fermion mean-field theory for the $\sigma t$-$J$ model, whose projective symmetry group (PSG) is selected based on numerically determined hopping and pairing correlations, and show that it explains the robust spin pseudogap upon doping.

Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax imputation, including one-dimensional Convolutional Neural Networks (1DCNNs), Long Short-Term Memory (LSTM) networks, and conventional machine learning models. We examine physics-informed input augmentation, temporal modeling, and transfer learning using synthetic RAFT and STORM datasets for pre-training and observational IBTrACS data for fine-tuning. Including the radius of 34-knot winds (R34) substantially improves performance across all model types. Temporal models achieve higher average correlations than non-temporal models despite using approximately an order of magnitude fewer samples, indicating better preservation of relative Rmax variability across storms. This advantage is more pronounced when R34 is unavailable, suggesting temporal information can partially compensate for missing storm-size predictors. Transfer learning does not improve performance, likely because synthetic datasets have lower and less variable Rmax distributions than IBTrACS. These findings demonstrate the potential of temporal deep learning for reconstructing incomplete TC records and highlight the importance of physics-informed inputs, observational data availability, and distributional consistency in coastal hazard assessment.

We investigate effects of uniaxial stress to magnetic orders and electrical resistivity of the centrosymmetric magnetic skyrmion compound Gd$_2$PdSi$_3$, which has a hexagonal crystal structure composed of triangular lattice layers of magnetic Gd$^{3+}$ ions. This compound is known to exhibit the triple-$q$ magnetic skyrmion lattice phase with a giant topological Hall effect in the first field induced phase [T. Kurumaji $\textit{et al}$. Science $\textbf{365}$, 914-918 (2019)]. In contrast to the established picture of the field-induced phase, the ground state of this system still remains to be studied. Although previous studies reported the existence of the incommensurate magnetic modulations described by a magnetic modulation wave vector ${\bf q}=(q,0,0)$ where $q\sim 0.14$ and its equivalents, it is still unclear whether the magnetic structure is a single-$q$ structure or a multiple-$q$ structure. In the present study, we performed magnetization, resistivity and neutron diffraction measurements with a compressive uniaxial stress applied perpendicular to the $c$ axis. The observed data revealed that the system did not exhibit anisotropic magnetic and electric properties expected from a single-$q$ magnetic order, suggesting that the magnetic ground state of this system is a multi-$q$ magnetic order.

We present the first spectroscopic follow-up for TOI-7475, and report the detection of a planetary-mass companion. Transits with a period of 3.25 days were observed by TESS. The dips in brightness are 0.006 mag and last approximately 4.5 hours. The host star is a spectral type F5 with a mass of approximately 1.48 M$_{\odot}$. We collected 24 high resolution echelle spectra using the PST1 telescope. Analysis of the TESS photometric data and our radial velocities was performed using the Wilson-Devinney method. We obtained a planetary mass of the companion of 12.0$\pm$1.0 M$_{\mathrm{jup}}$. The small semi-major axis and short orbital period classify the planet as a Hot Jupiter. Its orbit is elliptical with an eccentricity of 0.16. The ellipticity is evident in the spectroscopic data and could indicates the young age of the system.

We present constraints on the nuclear equation of state (EOS) from microscopic asymmetric matter calculations at zero temperature based on chiral nucleon-nucleon and three-nucleon interactions. The constraints include the saturation point, the isospin dependence of the incompressibility, and the symmetry energy, as well as the crust-core transition density of neutron-star matter. To quantify and propagate correlated uncertainties from noisy many-body calculations to derived observables, we introduce GPDiff, an efficient JAX-based Python package for multivariate Gaussian process (GP) regression with automatic differentiation. After training, GPDiff enables joint predictions of the EOS and derivatives of arbitrary order with respect to the input variables, including mixed partial derivatives. In this initial application, we analyze recent high-order many-body perturbation theory calculations of asymmetric matter up to about twice saturation density and explore nonstationary change-surface kernels, a class of input-dependent kernels, for modeling the EOS. GPDiff is broadly applicable to microscopic nuclear EOS calculations at zero and finite temperature and provides a versatile package for GP-based uncertainty quantification and inference of the nuclear EOS.

Quantum algorithms for nonlinear partial differential equations remain challenging because nonlinear dynamics are not directly amenable to unitary quantum simulation. Building on the level-set formulation, we construct a quantum algorithm and provide an explicit gate-level implementation for solving scalar conservation laws. The nonlinear equation is first lifted to a linear Liouville equation, discretized by finite differences, and then embedded into a unitary evolution through Schrödingerisation. We further develop quantum procedures for estimating relevant observables from the evolved state. Error bounds and gate-complexity estimates are established for the complete algorithm. The resulting complexity comparison demonstrates a quantum advantage for observable estimation in sufficiently high spatial dimensions, under standard assumptions on state preparation and oracle access. Finally, numerical experiments validate the accuracy, multidimensional applicability, and predicted scaling of the proposed method.

Practical quantum key distribution (QKD) receivers may leak information about their measurement outcomes and settings to the channel (e.g., through detector backflashes or back-reflected Trojan-horse light) that could compromise the protocol's security. Here we present a simple finite-key security proof based on phase-error estimation for prepare-and-measure QKD in the presence of either a priori information leakage about the basis choices and/or a posteriori information leakage about the measurement outcomes. The proof requires only a bound on the distinguishability of the side-channel states. Furthermore, the analysis is modular and compatible with existing security proofs that address detector and source imperfections, making it applicable to a wide range of practical QKD implementations.

Efficient thermal management of beam-intercepting devices is essential for high-power heavy-ion accelerators,where intense and localized energy deposition can limit primary beam power and operational reliability. This work presents the thermal design and experimental validation of a water-cooled bimetallic minichannel beam dump developed for the Facility for Rare Isotope Beams (FRIB), a leading experimental nuclear physics facility. The design integrates three key thermal features: a tilted absorber geometry to reduce local heat flux, a CuCrZr/Al2219 bimetallic structure to enhance heat spreading while maintaining water-side compatibility, and 2-mm-wide minichannels to achieve high convective heat removal. A conjugate thermal model was developed to predict surface temperature, internal temperature gradients, bimetallic-interface temperature, coolant temperature rise, and thermal margin under representative beam-loading conditions. The prototype was validated in vacuum ($\approx$5$\times 10^{-4}$ torr) using a 17-keV electron beam. Surface temperatures were measured by infrared thermography, and internal temperatures were measured using embedded thermocouples. The calculated surface temperature distributions were generally consistent with the measurements within the experimental uncertainty of 4$\%$ in absolute temperature for central beam irradiation, while internal temperature measurements showed reasonable agreement within 15$\%$, mainly due to uncertainties in thermocouple placement and beam-position calibration. The validated model confirms that the minichannel beam dump can maintain absorber temperatures within the design limit under intermediate-power FRIB operating conditions. These results demonstrate the effectiveness of bimetallic minichannel cooling for compact, high-heat-flux beam dump systems in heavy-ion accelerator facilities.

We study effective spin interactions arising from quantum electrodynamics (QED) scattering between localized fermionic spins. By integrating out photon and mediator fields, the dynamics reduce to an effective spin Hamiltonian. For two qubits in the nonrelativistic regime, the resulting interaction takes a tensor dipolar form with an asymptotic decay proportional to \(R^{-3}\). We obtain analytical expressions for the entanglement negativity, highlighting its dependence on coupling strength and spatial configuration. We then examine a setup in which two bath spins interact via a sequential exchange with an intermediate fermionic mediator. At the perturbative order considered, the mediator remains unentangled and induces an effective bath–bath interaction with stronger spatial suppression than in the photon-mediated case. Extending the construction to an \(N\)-spin setting yields an effective interaction network mediated by virtual exchange processes, which can support the generation of multipartite entanglement across the system.

Correlations that do not admit a deterministic explanation are a central feature of quantum theory and a key resource for quantum information processing. Identifying and certifying such correlations, however, remains a fundamental challenge. In this work, we advance on this problem by considering deterministic correlations in the prepare-and-measure scenario consistent with a wide range of communication restrictions. To certify correlations incompatible with such deterministic models, we ask whether they admit a scenario in which measurement outcomes can be perfectly predicted by an adversary equipped with classical side-information. We show the usefulness of this approach and propose semidefinite programming relaxations tailored to three representative communication restrictions: fixed ensemble, bounded overlaps and restricted observables.

We aim to investigate the tidal disruption and gravitational wave events shortly after a massive black hole binary merges in the galactic centre and whose remnant black hole is ejected from the galaxy. Using computational methods, black holes of mass $M_{\bullet}=10^{5}$ M$_\odot$ and $4\times10^{5}$ M$_\odot$ embedded in a nuclear star cluster are kicked at velocities of $v_k=300$ and $600$ km s$^{-1}$. Systems are integrated for $0.1$ Myr using a $4$th-order Hermite scheme. The kick instantaneously repopulates the loss cone, producing a strong burst of tidal disruption events and gravitational wave mergers. The anisotropy in the apsidal orientation of bound stars prolongs this burst phase. Rates increase for lower $v_k$, larger $M_{\bullet}$ and for steeper nuclear star cluster density profiles at moment of merger. Assuming binary black holes scour a Bahcall-Wolf density profile during coalescence, ejected remnants with mass between $10^{5}\leq M_{\bullet}$ [M$_\odot] \leq 4\times10^{5}$ generate observable offset events at a forecasted rate of $\dot{N}\lesssim290$ yr$^{-1}$ up to redshift $z=3$. If, at the moment of merger, the milliparsec scales of the nuclear star cluster are described by a shallow density profile ($\gamma=1$), this decreases to $\dot{N}\lesssim30$ yr$^{-1}$. The strong dependence on the initial density profile and recoil kick makes observables powerful probes of the nuclear star cluster post-massive black hole binary coalescence, and provides test for numerical relativity.

Fluid dynamics workloads are dominated by repeated solves of large, structured linear systems, motivating the search for quantum acceleration. The Variational Quantum Linear Solver (VQLS) is a leading near-term candidate, but practical deployment on hybrid quantum–high–performance computing (HPC) systems faces three persistent challenges: (i) the linear-combination-of-unitaries (LCU) encoding of the system matrix explodes in memory and runtime as the problem size grows, (ii) ansatz selection is largely empirical, with no clear link between standard circuit metrics and solver convergence, and (iii) end-to-end VQLS pipelines have rarely been exercised on production HPC hardware at non-trivial qubit counts. This work addresses these challenges through three contributions. First, we benchmark four matrix-encoding strategies—naive LCU, PennyLane-integrated, Fast Walsh–Hadamard Transform (FWHT)-based parallel Pauli decomposition, and an singular value decomposition (SVD)-based two-term LCU—and show that the FWHT approach reduces peak memory by up to $1298\times$ on an $11\times 11$ Hele–Shaw grid, while the SVD-based coherent VQLS delivers over $10{,}000\times$ per-iteration speedup over standard Pauli-based VQLS at 8 qubits. Second, we evaluate 11 ansatz families with gradient-free and gradient-based optimizers on canonical Hele–Shaw flow, and find that expressibility and entanglement metrics correlate only weakly with VQLS convergence, motivating problem-aware ansatz design. Third, we deploy the full workflow on the OLCF Frontier supercomputer and successfully simulate a 15-qubit tridiagonal Toeplitz system on a single node. Together, these results establish a practical baseline for VQLS in hybrid quantum–HPC computation fluid dynamic (CFD) workflows and identify the remaining bottlenecks for larger problems.

A key challenge when designing hybrid quantum-classical workflows is the identification of quantum candidates, that is, determining for which specific problems quantum advantages could potentially be achieved. When choosing between several candidates, it is crucial to consider the characteristics specific to the problem, as these can fundamentally determine how successful quantum or classical approaches will be. This implies that specialized expertise is needed to use hybrid quantum-classical workflows successfully. To address this challenge, we propose a domain-specific language (DSL) to express best-practices in solution strategies using a universal representation that is easy to use and share. This DSL provides a flexible approach to design hybrid quantum-classical workflows and to automate decisions for a wide range of problems, supporting decisions down to problem-specific details while remaining technically independent. Furthermore, we propose a framework that is built around our DSL that enables the execution of defined workflows using the ProvideQ toolbox as an orchestration layer. All contributions from this publication are open source.

Aims. This work aims to introduce a new method of interferometric measurements based on correlation of Wigner functions constructed for the fields from a distant source. Methods. This work presents theoretical studies of correlation of the Wigner functions. Numerical simulations support the findings. Results. It is shown that in comparison to the correlation of the fields, a correlation of their Wigner functions samples twice higher spatial frequencies of the source intensity distribution and has about twice smaller scale of the spatial pattern. This opens a fundamental possibility to improve the angular resolution of interferometric measurements by a factor of two.

In the Noisy Intermediate-Scale Quantum (NISQ) era, limited qubit availability and hardware noise constrain the practical deployment of quantum machine learning (QML). Existing quantum neural network (QNN) and quantum convolutional neural network (QCNN) architectures often require increasing quantum resources as the input dimension grows, limiting scalability on near-term devices. We propose QSVM-RQNN, a low-qubit framework integrating centroid-based Quantum Support Vector Machine (QSVM) similarity learning with Recurrent Quantum Neural Networks (RQNNs) for fault classification. The framework reduces the feature space using principal component analysis (PCA), partitions the reduced representation into sequential timesteps, and processes them using a compact three-qubit recurrent quantum architecture with shared parameters. Two complementary variants are developed: QSVM-RQNN-V1 performs class-conditioned joint quantum encoding of input and centroid segments, whereas QSVM-RQNN-V2 performs recurrent learning over timestep-wise quantum similarity representations. Experimental evaluation on multiple fault diagnosis datasets shows competitive and, in several cases, state-of-the-art performance compared with QSVM, QNN, QCNN, QSVM-QNN, QSVM-QCNN, and RQNN models. The proposed architectures provide favorable performance-efficiency trade-offs, improved recall, and enhanced fault detection on highly imbalanced datasets. These results demonstrate that integrating centroid-based quantum similarity learning with low-qubit recurrent representation learning provides an effective and scalable approach to condition monitoring and fault classification on resource-constrained NISQ devices.

Solar flare hard X-ray imaging is a key diagnostic of flare energy release and electron acceleration. The Hard X-ray Imager (HXI) aboard ASO-S compresses the two-dimensional source distribution into counts measured by 91 sub-collimators, making image reconstruction an inherently underdetermined inverse problem. Conventional algorithms such as CLEAN rely on point-source priors and manual tuning, whereas recent deep-learning methods offer no guarantee that their reconstructions obey the instrument's modulation-sampling forward equation. In this work we show that the counts decompose into two nearly decoupled quantities—the counts average energy, which tracks the total source flux, and the normalized counts distribution, which encodes the source spatial structure—and we exploit this property to construct a physics-constrained network, the Hard X-ray Imager Deep Learning Algorithm 2 (HXI-DLA2). Non-negativity and exact counts-average-energy closure are enforced at the network output, while a distribution-consistency loss aligns the re-projected counts with the measurement, so that the reconstruction satisfies the forward equation by construction. Tests on simulated Gaussian sources, observed soft X-ray morphologies, and a real HXI flare event show two main improvements over existing methods: the limiting resolvable dynamic range of double sources is pushed well beyond that of conventional imaging algorithms and our previous method; and complex morphologies on which prior reconstructions degrade, such as ring-like and diffuse structures, are reliably reconstructed, with the real-event result consistent with contemporaneous SDO/AIA imaging. Embedding the instrumental forward equation as a hard constraint while learning source priors from data offers a general inversion framework for modulation imaging.

Janus MXenes provide a chemically asymmetric platform to tailor the properties of two-dimensional transition metal carbides. Here, we present a systematic first-principles study of the structural, electronic, mechanical, and magnetic properties of Janus ZrMCX2 MXenes (M = Cr, Hf, Nb, Sc, Ti; X = F, Cl, S). All compositions exhibit negative formation energies and satisfy the Born stability criteria, supporting the energetic and mechanical stability of the Janus structures. In contrast to the metal-tosemiconductor transitions frequently reported for functionalized MXenes, we find that the metallic character is preserved in nearly all ZrMCX2 systems, despite the strong chemical asymmetry introduced by the Janus configuration. Surface functionalization nevertheless plays a key role in modulating the electronic density near the Fermi level and strongly influences the magnetic and mechanical responses. Several compounds exhibit ferromagnetic or antiferromagnetic ground states, three display halfmetallicity, and sizable magnetic anisotropy energies are obtained, with Neel temperatures exceeding room temperature in selected cases. Functionalization also enhances the elastic stiffness of the Janus structures. These results demonstrate that chemical asymmetry and surface termination provide effective control over magnetic and mechanical properties while maintaining a robust metallic framework, identifying Zr-based Janus MXenes as promising model systems for metallic and spindependent two-dimensional materials.

Magnetic order with vanishing net magnetization can produce non-relativistic spin-split bands, broadly categorized into even-parity altermagnets and odd-parity \(p\)-wave magnets. Here, we introduce a spin-bond theory that unifies these seemingly distinct phenomena into a single algebraic framework. We demonstrate that non-relativistic spin textures are fundamentally governed by two components of the electronic bond: unitary spin phases and Hermitian spin amplitudes. The unitary sector generates odd-parity p-wave and emergent spin-orbit-like textures, while the Hermitian sector generates even-parity spin fields, including the uniform \(\Gamma\)-split and bond-structured altermagnetic limits. Beyond unifying known phases, our theory uncovers a mixed non-commuting regime that emerges when the unitary and Hermitian sectors fail to commute, revealing an underlying non-commuting spin-bond structure. This regime generates a non-coplanar spin texture characterized by an even-in-momentum transverse spin polarization, providing a direct spectroscopic fingerprint for spin- and angle-resolved photoemission spectroscopy. Furthermore, we establish that this synthetic spin-orbit coupling can be dynamically tuned by geometrically controlling the non-commutation of the bond sectors. By providing a microscopic foundation for such tuning, our theory paves the way for advanced applications, including field-free spin qubits.

Projective measurements are foundational for quantum physics in general and quantum information tasks in particular. However, their direct implementation is often not warranted. Here, we investigate under what conditions continuous monitoring that manifests in quantum jump trajectories realises projective measurements over time. Considering finite-dimensional quantum systems and single, diagonalisable jump operators, we show if and how the detected quantum jump statistics force, and allow inferring, the convergence of individual quantum trajectories towards eigenstates of an observable in the long-time limit. We identify a necessary non-degeneracy condition that is related to the presence of a strong symmetry with non-degenerate symmetry sectors. We derive analytical expressions for the rate of convergence and the time-error relationship in finite-time measurements. Our results provide a transparent framework for understanding the emergence of projective measurements from continuous monitoring, with direct implications for the optimisation of quantum measurement protocols in experiments.