Papers

Understanding the speed with which various classes of quantum systems can scramble information is a key goal of quantum information theory, with relevance to both fundamental physics and the study of the efficiency of quantum algorithms. Famously, such understanding enabled Hayden and Preskill to show that, under the assumption that black hole dynamics may be modelled by a Haar-random unitary, old black holes act as ”mirrors”, reflecting infalling quantum information back out again effectively instantaneously. In this work, we investigate which aspects of this mirror behaviour survive beyond the Haar-random setting by considering a random free fermionic model of black hole dynamics. We find that the rate of information release is governed by the underlying representation-theoretic structure; such a black hole acts as a ”polarised mirror”, retaining information encoded in low fermionic-degree irreps while reflecting information encoded in high fermionic-degree irreps much more rapidly. As a result of the retention of low-degree information, we show that faithful recovery of the infalling quantum information is impossible until all but a vanishing fraction of the black hole has evaporated. However, augmenting the dynamics with a fixed Clifford unitary can restore their reflective behaviour, even though the ensemble's frame potential remains unchanged and far above its Haar value. Remarkably, we find that such black holes can outperform their fully Haar-random counterparts: in the high-fidelity limit, recovering a single-qubit message requires asymptotically approximately $37\%$ fewer emitted qubits.

Uncertainty quantification (UQ) for random partial differential equations (PDEs) is ubiquitous in computational science and engineering. However, classical spectral solvers for this class of problems face the curse of dimensionality, and existing neural solvers often ignore the stochastic structure that makes moments and calibration tractable. We introduce a stochastic separable physics-informed neural network, dubbed S$^{2}$-PINN, that represents the solution $u(t,\mathbf{x},\mathbf{Z})$ of a random PDE with a learnable Gaussian spatial dictionary, Fourier temporal features, and a generalized polynomial chaos (gPC) stochastic basis, coupled by a low-rank Canonical Polyadic (CP) tensor decomposition core. The method is trained with a hybrid strong-form and gPC-projected residual loss. Our theoretical analysis establishes that the separable class is dense in $L^2$ under mild conditions, and the projected residual corresponds exactly to a stochastic Galerkin constraint. Furthermore, we show that mini-batch projection coefficients are logarithmically dependent on the number of gPC modes, and that the orthogonality penalty controls the conditioning of the learned spatial dictionary. Using four manufactured random PDE benchmarks, we show that S$^{2}$-PINN outperforms nine baselines in terms of mean and variance accuracy, as well as calibration, while using significantly fewer parameters. Further evaluations on non-manufactured Poisson and Darcy problems, a stochastic Navier–Stokes problem, a diffusion scaling study of higher random dimensions, and two stochastic inverse problems reveal the generalization capabilities of the proposed structure. Together, these results support stochastic separability as an effective design principle for physics-informed neural UQ. The code for the experiments can be found in https://github.com/DMax1314/s2pinn

We construct a finite cyclic $L_3$ presentation (that is, a cyclic $L_\infty$ algebra with brackets vanishing above arity three) of the variational deformation theory defined by the heterotic $G_2$ superpotential near a torsion-free standard embedding, to first order in $\alpha'$. Auxiliary fields for the inverse coframe, spinor derivative, flux and induced tangent connection give a shifted-cotangent Hamiltonian of degree at most four. Eliminating the auxiliary fields recovers the physical functional. On the flat seven-torus, this functional has a nonzero quintic Taylor coefficient in local physical spinor coordinates. For $Y=S^1\times X$, with $X$ a Calabi–Yau threefold, we restrict to the product locus with horizontal gauge and gerbe data. The functional reduces to $ \frac L4\operatorname{Im}\int_X(H+i\,d\omega)\wedge\Omega, $ where $L$ is the circle length and $\Omega=e^{-2\Phi}\Psi$ is the weighted complex volume. The induced $SU(3)$ structure selects a Hermitian tangent Courant subalgebroid. Transverse isotropic lifts in its complexification encode the holomorphic connection and string equations, together with the induced tangent connection, weighted canonical line and gerbe hierarchy. Their Taylor brackets form an $L_3$ algebra whose string part agrees with the known holomorphic heterotic algebra. We then show that this holomorphic theory is obtained by characteristic Hamiltonian reduction of a finite contractible stabilization of the product variational theory, in every cochain degree. While the quotient captures the holomorphic equations, conformal balance, gauge primitivity and leading-flux admissibility are additional conditions on a real representative.

We conducted eMERLIN and Gemini integral field unit (IFU) observations towards TXS 1033+026, a post-merger ultraluminous infrared galaxy, to study the radio continuum and $\rm H\alpha$ and HI 21 cm lines at sub-kiloparsec scales. We find a symmetric, two-sided core- jet structure in both L-($\approx 1.4$ GHz) and C-($\approx 4.8$ GHz) band continuum images. The active galactic nucleus likely has a double-double radio morphology, indicating possible multiple episodes of activity. The Gemini IFU observations provided sub-arcsecond resolution. Results from these observations suggest the presence of a kiloparsec-scale circumnuclear, warped $\rm H\alpha$ disc that rotates around the core. The HI 21 cm maps reveal a complex kinematic structure, with indications of two counter-rotating gas components orbiting the core-jet axis; one of these components counter-rotates relative to the kiloparsec-scale $\rm H\alpha$ disc. However, the true morphology of this structure may be more complex than our interpretation suggests. We estimate a HI 21 cm velocity-integrated optical depth of $\rm 90 \pm 3 \ km \ s^{-1}$. We note that these are the first results to probe $\rm H\alpha$ and cold neutral hydrogen gas in TXS 1033+026 at sub-kiloparsec scales.

Purpose: This study extends our fluid-structure interaction (FSI) framework for surgical bioprosthetic heart valves in an experimental pulse duplicator to a transcatheter aortic valve replacement (TAVR) device. We assess hemodynamic and leaflet kinematic agreement across pulse rates and the transferability of calibrated boundary-model parameters. Methods: We compared simulated and experimental pressure, volumetric flow rate, projected dynamic valve area (PDVA), and leaflet kinematics at 60, 70, and 80 beats per minute (bpm). Reduced-order boundary-model parameters were calibrated using only data acquired at 70 bpm and were subsequently held fixed. At each pulse rate, a condition-specific pump-pressure waveform was constructed from the measured upstream pressure and flow rate. The 60 and 80 bpm cases therefore evaluate parameter transfer under measurement-informed upstream forcing. Results: Across the three conditions, normalized root-mean-square errors are 2.88-3.08% for aortic flow rate, 7.64-8.07% for upstream ventricular pressure, 3.50-6.21% for downstream aortic pressure, and 3.07-4.70% for PDVA. The simulations reproduce the principal pressure, flow, valve-opening, and valve-closure features. The downstream aortic pressure and PDVA measurements at 60 and 80 bpm were not used to construct the upstream forcing or recalibrate the model parameters and therefore provide the principal out-of-calibration validation evidence. Conclusion: The results provide conditional experimental bench validation evidence for downstream hemodynamics and valve dynamics under measurement-informed upstream forcing and support boundary-parameter transfer across pulse rates without recalibration. This work advances an experimentally supported, high-fidelity FSI framework for evaluating TAVR device performance.

An analog device implements a local Hamiltonian H, but a polynomial P (H) is in general not local, so the device cannot implement it. We carry P (H), to any prescribed accuracy, as the action of a single time-independent local Hamiltonian on an explicitly described invariant subspace. Short chains of ancilla qubits are attached to H. A chain of 2m sites has a unique isolated eigenvalue that is an analytic function of the input vanishing to order exactly 2m, because the input must cross the chain and come back before it can shift the energy at the far end. Chains of different lengths therefore form a triangular family, and a weighted sum of them reproduces a prescribed polynomial term by term. Even chains give the even part and odd chains the odd part, so any polynomial is reached. We prove uniqueness, analyticity and coefficient bounds uniform in the chain length for inputs H of norm below any r < 1. Where the circuit model pays degree 2l in 2l sequential oracle calls, the result here is one Hamiltonian of locality two more than that of H, which pays the degree in O(l^2 + log2^(1/eps)) ancillas and in energy scale. As an application we filter a marked eigenstate of a local Hamiltonian. Composing a synthesised square along the Chebyshev doubling identity works, but its energy scale grows quasi-polynomially with the degree. Iterating instead the exact eigenvalue branch of the two-site chain k times gives a filter on k + 1 ancilla qubits whose error decays geometrically in k, at an energy scale polynomial in the inverse passband width and independent of k, the locality growing by one per stage. In adiabatic optimisation with a rank-one driver, the construction replaces that non-local driver, by a Hamiltonian with O(log n) ancillas and O(log n)- body terms, at an energy scale polynomial in n. Simulations including every ancilla reproduce the spectrum of the ideal algorithm.

In this work, we investigate final-state interaction effects in $B^{\mp}\to K^+K^- \pi^{\mp}$ decays above 3 GeV with a twofold objective: (i) to study $CP$ asymmetry, and (ii) to search for possible signatures of the $X(3700)$ state (also denoted as $X(3720)$). We consider both tree-level and resonant contributions, with the latter arising from a triangle mechanism $B^{\mp} \to \bar{D}^0 D^{\ast \mp} (\to \pi^{\mp} D^0) \to K^+K^- \pi^{\mp}$, in which the $D \bar D \to K \bar K$ rescattering process carries the signature of the exotic $X(3700)$ state. We analyze the dependence of our results on the relative phase between the tree-level and resonant contributions, as well as on other relevant parameters. We estimate the differential mass distribution in the region where the $X(3700)$ may leave a trace, and the $CP$ asymmetry driven by final-state interactions. Our findings are compared with available data for the differential mass distributions and the $CP$ asymmetry. Taken together, these results may contribute to a better understanding of the properties of exotic states and the role of $CP$ asymmetry in $B$-meson phenomenology.

During zirconium carbide oxidation, liberated carbon can remain beneath the oxide or escape as gas, complicating the interpretation of mass measurements. We adapt the small-cell ensemble method using sixteen 140 ps ab initio molecular dynamics trajectories at 873-1273 K under a sustained high oxygen flux with gas removal. The average rates of net mass gain and total carbon transfer into gas or retained chains follow Arrhenius trends. Above 973 K, gas evolution levels off while total carbon transfer increases. The fraction of transferred carbon retained in chains rises from 40 % at 973 K to 66 % at 1273 K. Fits after 40 ps suggest declining carbon transfer rates over time, although not in every trajectory. These results show that carbon partitioning during early oxidation depends on temperature, with increasing temperature above 973 K favouring chain formation over gasification.

The $^{45}$Sc($p,\gamma$)$^{46}$Ti reaction plays an important role in both hydrostatic and explosive nucleosynthesis. During hydrostatic oxygen and silicon burning in massive stars, including dynamic processes such as convective carbon-oxygen (C-O) shell mergers, it acts as a bottleneck that regulates the reaction flow in the Sc-Ti mass region. In oxygen-neon (ONe) novae, the ejecta indicate the production of elements up to the Fe group, as observed in V1974 Cygni, and the $^{45}$Sc($p,\gamma$)$^{46}$Ti reaction links nuclei above Ca to the Fe region. In core-collapse supernovae (CCSNe), it influences the production of the radioactive isotope $^{47}$Sc. At present, the thermonuclear reaction rate is based on statistical model calculations. In this work, we report on measurements of the $^{45}$Sc($p,\gamma$)$^{46}$Ti reaction performed using the 5~MV accelerator at the Nuclear Science Laboratory, University of Notre Dame, over a laboratory proton energy range of 526–1275~keV. We present the first direct measurements of the resonance strengths for six resonances at $E^{\rm lab}_{\rm R} = 917.4$, 1026.0, 1031.1, 1049.7, 1059.4 and 1257.5~keV, together with integrated resonance strengths at $E^{\rm lab} = 555$, 630, 700, 761 and 871~keV. For resonance energies below the lowest directly measured energy, available transfer-reaction data were used to estimate the resonance strengths. An updated thermonuclear reaction rate is derived, and its astrophysical implications are discussed.

Information Processing Capacity (IPC) is a powerful system-agnostic metric for quantifying a dynamical system's computational capability and is widely used in quantum reservoir computing. However, standard IPC suffers from severe computational limitations. Computing IPC involves estimating how well the system can reconstruct, via linear regression, an (in principle) infinite family of orthogonal polynomial functions of delayed inputs. In practice, this evaluation must be made finite, so we need only to consider polynomials up to a chosen maximum degree. Moreover, the computational complexity for computing IPC grows combinatorially with the considered maximum degree and delay. Here, we introduce Kernel-IPC (KIPC), a scalable framework that uses kernel methods to compute the total IPC without explicitly enumerating basis functions. This allows us to account for the full (infinite) basis (without degree truncation). We also provide a null-hypothesis test for statistically comparing the KIPC of two dynamical systems. We validate KIPC on an echo state network and demonstrate its utility for quantum reservoir computing on a disordered spin chain.

A central topic in economics and finance is the valuation of strategic games and bets, of investments providing uncertain future cashflows, and of other asset-like structures and contracts. Often, such valuation involves a probabilistic model, random variables, and computing expectation values of complex payoffs. Extending beyond this classical framework, a scenario emerges where assets are non-commuting operators and the inherent uncertainty is described by quantum states. In this work, we combine such notions of quantum finance with a graph-theoretic framework and ideas from contextuality. We discuss exclusive quantum assets and introduce scenarios when holding such assets can provide advantages over holding classical assets. In particular, we consider a "quantum market" based on nonlocal games such as the CHSH game and demonstrate that this setting can function as a market with well-defined financial structures. We provide upper bounds on the fair prices of the quantum assets based on graph-theoretic quantities like the independence number and the Lovász theta number. Our insights into markets of non-commuting assets help to elucidate what a financial system in a world filled with quantum technology could look like.

Power grids are beginning to host AI data centers whose demand can change abruptly and in a correlated way, and operators and planners must decide whether existing controllers can absorb the resulting transients and where new flexibility is worth installing. We cast this as a question in stochastic control: how far from optimal is an implementable controller? For controlled diffusions with affine, state-independent actuation, additive and possibly degenerate noise and quadratic control cost, any Hamilton-Jacobi-Bellman (HJB) subsolution bounds the optimal cost from below and simulation bounds the deployed cost from above, so their gap certifies the permissible suboptimality. We construct subsolutions from path-integral control by completing the control geometry: enlarging the control Gramian until it matches the physical noise makes the problem linearly solvable, and its Feynman-Kac value is an automatic lower bound whose HJB residual is exactly the energy of the fictitious control. A dual noise-deflation construction can be tighter but requires a curvature condition. The geometry yields planning rules: price control authority in proportion to local noise variance, and use shadow values to guide sparse reinforcement. For nonlinear stochastic swing dynamics of the IEEE 118-bus system after a severe load loss, a simple generator controller is certified within 2.8% of optimal under homogeneous forcing; under heterogeneous forcing the gap is 39% with uniform prices and 1.2% once the same total authority is repriced, before any hardware is added.

We explore the microscopic origin of THz-driven lattice dynamics in methylammonium lead bromide CH$_3$NH$_3$PbBr$_3$ using two-dimensional THz Kerr-effect spectroscopy. The analysis of the experiment enables discrimination between competing nonlinear excitation mechanisms and reveals that the dominant pathway is phonon-mediated rather than purely photonic. In particular, we argue that the commonly observed 1.15~THz Raman-active $A_g$ mode is not excited directly by the THz electric field. Instead, it emerges through cubic anharmonic coupling to an infrared-active phonon at 0.68~THz. Furthermore, a satellite feature at 1.36~THz is identified as a two-phonon Raman response of the same infrared-active mode. The observation of strong quadratic coupling involving the infrared-active phonon suggests an unconventional form of electron-phonon dressing present in lead-halide perovskites.

Gaussian Boson Sampling (GBS) has provided strong evidence of quantum advantage, motivating the search for real-world applications. Such applications rely on mapping physical problems to the native GBS output: photon-number patterns sampled from a highly non-trivial probability distribution. Here we derive a GBS-compatible mapping for the rate constant $k_\mathrm{IC}$ of internal conversion (IC), incorporating two important physical effects – Duschinsky rotations and Herzberg–Teller contribution. We validate this framework using sampling simulations of dibenzoterrylene, reproducing reported values of $k_\mathrm{IC}$. Beyond $k_\mathrm{IC}$ reconstruction, we show that the photon-number patterns of GBS encode the vibrational mode combinations responsible for the IC process, which we refer to as non-radiative pathways. This pathway-resolved data can then be grouped in different ways to extract multiple physically motivated observables. In particular, it enables $k_\mathrm{IC}$ analysis at several levels, from individual modes and mode family contributions to recurring vibrational configurations shared by multiple pathways. We find that Duschinsky rotations increase the fraction of $k_\mathrm{IC}$ associated with correlated vibrational pathways from 46% to 86%, and identify the mode combinations with the largest rate contributions. These pathway-resolved insights can guide targeted molecular modifications, including selective deuteration and bond substitution, to control non-radiative decay.

In this article, we propose a new method to calculate the angular momentum distribution of fis- sion fragments adapted to the case of 2-center axial basis used to solve the Hartree-Fock-Bogoliubov equations in view of fission studies. As a first application, we have studied the properties of the fission fragment angular momentum distribution in the scission area of 240Pu using the Gogny D1S interaction. Here, a one dimensional asymmetric and adiabatic path (the lowest in energy), gener- ated by the newly proposed Link and Drop methods, has been considered. The major result of this study is the appearance of an odd-even angular momentum staggering in both fragments when going through scission. The odd-J components are progressively suppressed as the fragments separate, leading to an increasing dominance of even-J components. This behavior is systematically correlated with the rapid reduction of the intrinsic octupole deformation of the fragments, asso- ciated with the restoration of a more nearly reflection-symmetric shape. Finally, particle-number projection reveals the role of shell effects associated with specific fragment configurations in shaping the fragment angular momentum distributions.

Quantum computing is a promising paradigm for problems that are challenging for classical machines, but realizing that promise requires far more qubits than a single processor can offer. Distributed quantum computing (DQC) scales out by connecting multiple quantum processing units (QPUs), at the cost of making entanglement the scarce resource: every remote gate consumes a Bell pair, and inter-QPU links generate Bell pairs at finite rates orders of magnitude slower than local gates. Since quantum programs are executed repeatedly, this rate bounds how fast shots complete and thus how fast results are obtained. Existing DQC compilers emit a single implementation per circuit, so throughput is capped by its busiest link while other links stay idle. We observe that alternative compilations of the same circuit are logically equivalent yet stress different links; executing them concurrently and pooling their samples converts idle Bell pairs into additional shots. We formulate the joint selection of compilations and allocation of shots under per-link Bell-pair capacities as Candidate-Constrained Max-Shot Allocation (CMA), prove it NP-hard, and solve it with a dynamic program and its approximate variant AppDP, a compact MILP, and a greedy heuristic Effi. In simulation on six-QPU networks, multi-compilation raises throughput by 2-4.5* over the single compilation and correspondingly improves output fidelity under equal Bell-pair budgets. On hardware, it increases measured fidelity by up to 93% and reaches the single-compilation fidelity in up to 10* less time. Scaling to 36 QPUs and 144-qubit circuits, MILP improves throughput over the single compilation by 76.8% on average across 48 configurations while Effi allocates in milliseconds.

Time-domain multiplexing of continuous-variable quantum states provides a scalable approach to photonic quantum information processing, in which individual temporal modes can be sequentially manipulated within a common optical path. Here we demonstrate pulse-resolved post-generation phase control of squeezed-vacuum states at a repetition rate of 93 MHz using a low-loss bulk lithium-niobate electro-optic modulator driven by a broadband RF power amplifier. The driving architecture exploits low-cost, commercially available RF components and the impedance mismatch between the RF chain and the capacitive crystal load, rather than relying on dedicated custom high-voltage electronics, to obtain large voltage excursions on the modulator while operating above the piezoelectric-resonance region of lithium niobate. The modulator is driven synchronously with the optical pulse train, enabling the phase of consecutive temporal modes to be individually controlled. An interferometric calibration yields a maximum differential phase excursion of 1.93 rad. We apply the modulator to a 1035 nm squeezed-vacuum field generated by a synchronously pumped optical parametric oscillator and characterize the output using time-resolved balanced homodyne detection. With the local-oscillator phase kept fixed, the phase of the squeezed state is varied from pulse to pulse, and the corresponding phase-dependent quadrature variance is reconstructed. The resulting quadrature scan agrees with an equivalent measurement obtained by varying the local-oscillator phase with a piezoelectric actuator. These results demonstrate post-generation quantum-state phase control at the individual-pulse level and provide an electro-optic building block for programmable manipulation of time-multiplexed quantum states in recirculating optical architectures.

Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge. In fluid dynamics, quantum-inspired methods based on tensor-network techniques have recently emerged as a promising new paradigm, offering large degrees of data compression on conventional hardware and a natural pathway towards solving exponentially large problems on quantum computers. Early studies suggest that tensor-network methods may also help overcome key bottlenecks in computational plasma dynamics, particularly for industry-relevant scenarios where conventional simulations remain costly. In this work, we present recent progress in developing tensor-network methods compared to standard particle-in-cell codes and discuss their capability to overcome the bottlenecks of conventional approaches. We further extend the tensor-network formulation to support the insertion of electromagnetic fields at arbitrary spatial locations. This capability is demonstrated through the injection of a laser field to accelerate a plasma, providing a key step towards tensor-network-based simulation of externally driven plasma systems.

Poisson sprinkling provides a Lorentz-invariant way of generating causal sets from a continuum spacetime, but numerical work necessarily restricts the sprinkling to a finite region. For observables involving causal-set links, this finite-volume restriction can generate a bias that does not disappear by simply increasing the number of sprinkled points. The origin of the problem is geometric. At fixed proper time from an event in Minkowski spacetime, candidate links populate a non-compact hyperbolic space of rapidities. In dimensions $d+1$ with $d\geq 2$, this space is boundary dominated: the fraction of volume close to the boundary of a large region does not vanish in the infinite-volume limit. Different finite-volume regularizations can therefore produce different limiting normalized statistics. We relate this phenomenon to the failure of naive open-boundary thermodynamic limits on non-amenable spaces, derive explicitly the dependence of the link proper-time distribution on the finite cutoff, and distinguish a well-defined link-intensity profile from the ill-defined notion of a uniformly chosen link. We verify these results with independent Poisson-sprinkling simulations in $d+1$ dimensions with $d=1,2,3$. We then propose an event-centered product-window estimator with a controlled double-scaling limit, and discuss compact hyperbolic quotients as a possible boundary-free alternative.

The symmetry of a physical system given by the 122 magnetic point groups (MPGs) leads to only 21 sets of restrictions on any physical tensors. Those, so called, tenor forms were labeled by letters from A to U by R. R. Birss (Symmetry and Magnetism, North Holland, Amsterdam, 1964). A new concept of property-related point groups (PRPGs) is proposed. It is shown that the 21 PRPGs isomorphic to the 21 non-centrosymmetric crystallographic point groups (CPGs) corresponds to the 21 tensor forms. Therefore instead of a symbols from A to U one can use PRPGs as mathematical objects corresponding to the tenor forms. From the Neumann's principle given multipole component is only allowed in a system when a group describing symmetry of the system is a subgroup of the multipole component symmetry. Each tensor can be expressed in the terms of the multipole degrees of freedom, i.e. it corresponds to a subset of all multipole components allowed by given PRPG. Those relations leads to the algebra of tensor forms, where space of allowed tensor components and space of PRPG operators are somehow dual to each other.

Stimulated parametric down-conversion (StimPDC) is the seeded version of the process that underlies much of photonic quantum information. Replacing the vacuum at one input port with a coherent seed leaves the interaction Hamiltonian, the phase-matching function and the two-photon amplitude unchanged, while raising the output to levels accessible with ordinary classical detectors. This review treats the transverse spatial physics of that process. We show that a single relation, in which the idler amplitude is the pointwise product of the pump field with the complex conjugate of the seed, organizes results usually presented separately: the image and coherence transfer experiments of the 1990s; phase conjugation and its use for wavefront correction; the conservation and conjugation of orbital angular momentum, together with the generation of radial modes and geometric intensity patterns; and the processing of vector vortex beams carrying spatially varying polarization. We then consider StimPDC as an instrument rather than as an object of study, covering stimulated emission tomography, the experimental realization of Klyshko's advanced-wave picture, and the transfer of partial coherence, including the conservation of the twist phase of twisted Gaussian Schell-model beams. We close with the quantum regime, reached when the seed approaches the single-photon level or the idler is used as a herald, and with an assessment of open problems and prospective applications.

The cell cortex and cell adhesion contribute to the mechanics of epithelial cells and hence tissues. Vertex models of epithelia represent the cell cortex and cell adhesion, but these models mostly ignore the other subcellular structures that compose the cytoplasm surrounded by the cortex. Here, we reveal the mechanical consequences of these structures in a minimal vertex model of an epithelial monolayer, in which the single-cell energy includes a cytoplasmic contribution that represents, e.g., the cost of confinement of cytosolic polymers. Strikingly, our simulations show that the buckling transition of the monolayer under compression becomes discontinuous in an intermediate range of values of the associated cytoplasmic modulus. By asymptotic solution of this buckling problem in the continuum limit of the model, we explain this reentrant behaviour in terms of the tricritical points of a sixth-order Landau theory. Our results thus underline how cell-scale mechanics beyond the cortex and cell adhesion can change emergent, tissue-scale behaviour even qualitatively.

Collective noise can leave degrees of freedom unaffected, allowing them to store quantum information. Processing this information also requires physically available logical gates. We consider $r$ fundamental qutrits transforming under $U\in SU(3)$ and $s$ dual qutrits transforming under its complex conjugate $\overline U$, with collective action $R_{r,s}(U)=U^{\otimes r}\otimes\overline U^{\otimes s}$. Using the image of the walled Brauer algebra and Lie-algebra arguments, we show that adjacent exchanges within each qutrit type and one singlet-projector interaction between the two types generate any independently chosen family of special unitary gates on the noiseless subsystems by a finite pulse sequence, for every $r,s\geq1$. This establishes semi-universality of this system. We illustrate the result with a numerically optimised sequence of 24 pulses for three fundamental qutrits and one dual qutrit. We then use the encoding and available logical corrections for qutrit teleportation under correlated Weyl noise. Under explicit assumptions on channel composition and which trials are retained, and allowing for baseline implementation errors, we identify noise ranges in which encoded teleportation exceeds the classical fidelity bound of $1/2$, while the unencoded teleportation channel is entanglement breaking. We analyse fidelity on accepted trials separately from the transmission costs of the additional physical qutrits.

Quantum covert target sensing aims to detect low-reflectivity targets embedded in the thermal environment while avoiding discovery by an adversary. This covertness requirement constrains the admissible probe energy, thereby imposing a trade-off between covertness and sensing performance. However, how this resource constraint determines the ultimate estimation precision of the target parameter remains largely unexplored. To fill this gap, we establish a unified framework connecting covert target discrimination with quantum-enhanced parameter estimation. We first present perturbative $\epsilon$-covertness-induced bounds on the probe energy, which yield an admissible energy window narrowing as $\mathcal{O}(\sqrt{\epsilon/M})$. Moreover, we introduce the covertness-constrained quantum Fisher information (CCQFI) to quantify the ultimate precision of target-reflectivity estimation and assess its attainability with specific measurements. For Gaussian-distributed coherent-state (GCS) and two-mode squeezed vacuum (TMSV) probes, their CCQFIs scale as $\mathcal{O}(\epsilon)$ and $\mathcal{O}(M)+\mathcal{O}(\sqrt{\epsilon M})+\mathcal{O}(\epsilon)$, respectively. In particular, in the strong covertness regime $\epsilon\to0$, the GCS CCQFI vanishes, whereas the TMSV retains a finite $\mathcal{O}(M)$ contribution exhibiting a pronounced metrological advantage. Besides, photon counting saturates the GCS CCQFI, whereas homodyne detection achieves a higher attainability for the TMSV probe. Our work identifies covertness as a quantitative resource constraint governing the metrological limits of quantum target sensing.

Charge deposition in the particle-in-cell (PIC) method can be viewed as kernel density estimation on a spatial mesh. This allows the smoothing bandwidth to be selected through statistical criteria such as mean integrated squared error or cross-validation, but it also changes the electric field acting on the particles. Therefore, a selected bandwidth can alter the growth or damping of physical modes. We develop a dynamics-aware bandwidth-selection procedure that combines a density-error objective with constraints on specified linear modes. These constraints follow from the plasma equilibrium and the combined transfer of deposition, filtering and field gathering within the model rate-tolerance boundary. We demonstrate the need for equilibrium information through a controlled two-stream experiment. The controlled two-stream scan varies the beam drift while holding the initial spatial density, thermal samples, wavevector and fixed-filter transfer unchanged. Across four resolved equilibria approaching marginal stability, this filter reduces the measured growth rate by $0.7\%$ to $9.1\%$. Equilibrium-dependent interior settings satisfy the prescribed $10\%$ rate tolerance. We then apply the constrained selection rule to 85 Gaussian candidates, that reduces density and grid-field mean-square errors by $7.40\%$ and $3.70\%$ on independently evaluated snapshots. A separate projected-linear calculation at that exact width gives a ratio of mean individual growth rates of $0.9556$, with paired $95\%$ interval $[0.9453,0.9640]$, consistent with the prediction $0.9513$. Analysis of finite-marker loading and ensemble averaging explains how the measured rates relate to the response model. Together, these results demonstrate a filter-design procedure that combines statistical error reduction with subsequent validation of the selected linear dynamics.

Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depends strongly on the choice of quantum circuit architecture, a choice that remains largely manual. Most existing approaches rely on hand-designed or fixed circuit ansätze, requiring circuit structure, gate composition, and qubit connectivity to be specified in advance with no guarantee that they suit the task. This limitation is especially acute in image classification, where quantum circuits must transform features extracted by classical networks while remaining compact enough for practical training, requirements that generic, task-agnostic ansätze are unlikely to satisfy simultaneously. We extend EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification. EXAQC evolves PQCs as intermediate processing modules while retaining classical feature-extraction and prediction layers. On MNIST, Fashion-MNIST, and CIFAR-10, EXAQC achieves 98.42%, 90.62%, and 85.47% accuracy, respectively, while using comparable gate counts to other quantum architecture-search methods. Against classical networks, evolved hybrid models maintain comparable accuracy with substantially fewer trainable parameters, reaching 85.68% on CIFAR-10 with over 25$\times$ fewer parameters than a 10-layer CNN. Encoding choice also matters: rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10. These results demonstrate that automated circuit discovery yields compact quantum modules that can replace larger classical components in vision architectures while retaining competitive accuracy.

A method for solving the two-center Dirac equation using a Gaussian-product basis set tailored to the axial symmetry of diatomic systems is presented. For the ground state of H$_2^+$ at an internuclear distance $R=2.0~a_0$, we obtain a relativistic energy with relative precision at the $10^{-18}$ level. The properties of the basis set allow for an analytic Fourier transform of the wave function. As a first application we evaluate, for the first time, the zero-potential contribution to the one-loop self-energy for H$_2^+$ in Feynman and Coulomb gauges. Although this contribution is not separately gauge invariant, its calculation provides a proof of principle for the use of the present framework in prospective QED calculations.

Quantum error correction protects quantum information by encoding it in a code described by three numbers: how many physical qubits it uses, how many logical qubits it protects, and the smallest error it cannot detect. The last is an optimum over exponentially many candidates, so it is read off a solver rather than checked. Here we show that code parameters can be certified by a short certificate, a list or a pairing or a symbolic instance, whose correctness the kernel of a proof assistant decides by computation, and whose size is itself a theorem rather than an estimate. Two published code families have distances settled here: the bivariate bicycle code's $[[144,12,12]]$, certified, and the affine-permutation codes printed at $[[1152,580,\le 12]]$ and $[[2304,1156,\le 14]]$, settled at twelve and fourteen. Eleven code families and thirty-nine parameter sets follow, with nothing beyond three standard axioms trusted and the pipeline's specification step preset to twenty columns.

We establish lower bounds for Hamiltonian property testing with access to the time-evolution operator but not its inverse. Each experiment may query the time-evolution operator multiple times, and distances between Hamiltonians are measured in the normalized Frobenius norm. In this model, we show that testing whether a Hamiltonian is $k$-local or $\varepsilon$-far from every $k$-local Hamiltonian requires $\Omega(1/\varepsilon^2)$ total evolution time, matching the upper bound of Kallaugher and Liang (TQC'25). We also prove that testing whether an unknown Hamiltonian equals a target Hamiltonian or is $\varepsilon$-far from it requires $\Omega(1/\varepsilon^2)$ total evolution time, matching the upper bound of Sinha and Tong (2025). These are the first lower bounds for natural problems in Hamiltonian learning and testing that rule out Heisenberg-limited scaling of $1/\varepsilon$. As a third result, we show that amplitude estimation to precision $\varepsilon$ requires $\Omega(1/\varepsilon^2)$ total time evolution, recovering the result of Tang and Wright (QIP'26) in the continuous-time query model. All three results follow from the hardness of distinguishing the zero Hamiltonian from a suitably chosen ensemble of random Hamiltonians. We establish this hardness by adapting the continuous-time adversary method to forward Hamiltonian evolution.

We investigate how dissipation can emerge in a quantum network from temporal fluctuations of the interaction topology. For a network whose graph is redrawn at short time intervals, the averaged dynamics yields a Markovian master equation: the mean network fixes the coherent part and the covariance fixes the dissipative structure. The covariance eigenvalues set the noise strengths, while its eigenvectors determine the collective operators on which those strengths act. Two ensembles with identical mean adjacency and covariance spectra but different eigenvectors produce distinct dynamics despite identical purity. A physically realizable model yields an analytically predicted dissipative gap, giving quasi-protected dynamics. These results establish fluctuating connectivity as a resource for engineering quantum dissipation and protection.