Papers

Altermagnets host unconventional spin-polarized bands despite zero net magnetization, but controlling their spin structure remains challenging. We propose a multi-field approach to engineer spin polarization in $d$-wave altermagnets using gating, optical driving, and in-plane electric fields, which enable tunable and switchable polarizations along multiple directions. Optical driving induces out-of-plane ($z$) polarization, while gating and in-plane fields generate $x$- and $y$-polarizations via the Edelstein effect, all of which are experimentally detectable. We further find that spin- and band-selective doping induces chiral optical activity, a feature unique to altermagnets. Our approach provides a versatile route for full control of spin polarization in altermagnets.

Altermagnets have zero net magnetization yet feature spin-split bands. Here, we investigate how slow lattice vibrations (phonons) influence both the intrinsic and externally induced spin polarizations in two-dimensional $d$-wave altermagnets. For the induced spin polarization, we employ a Rashba continuum model with electron-phonon coupling (EPC) treated at the static Holstein level and analyze the spin Edelstein effect using the Kubo linear-response formalism to probe EPC-induced contributions. We find that, under a specific symmetry-lowering pattern such as a piezomagnetically active strain that explicitly breaks the inherent $C_4 \mathcal{T}$ symmetry, moderate-to-strong EPC progressively suppresses the induced polarization via both intraband and interband channels, with a threshold coupling marking the onset of complete spin Edelstein depolarization. The depolarization arises from a phonon-induced energy renormalization that leads to a complete collapse of the Fermi surface. While depolarization can occur even in the Rashba non-altermagnetic phase, it remains isotropic. The presence of altermagnetism makes it anisotropic and breaks the conventional antisymmetry between spin susceptibilities that occurs with pure spin-orbit coupling, rendering the effect highly relevant for spintronic applications. We further investigate how the phonon coupling to the altermagnetic order, Rashba spin-orbit strength, and carrier doping collectively tune the depolarization. Our findings demonstrate that static phononic effects offer a powerful means for on-demand control of spin polarization, enabling reversible switching between spin-polarized and depolarized states–a key functionality for advancing spin logic architectures and optimizing next-generation spintronic devices.

Phase transitions, sharp in the thermodynamic limit, get smeared in finite systems where macroscopic order-parameter fluctuations dominate. Achieving a coherent and complete theoretical description of these fluctuations is a central challenge. We develop a general framework to quantify these finite-size effects in synchronization transitions of generic stochastic, globally-coupled nonlinear oscillators. By applying a center-manifold reduction to the nonlinear stochastic PDE for the single-oscillator distribution in finite systems, we derive a mesoscopic description that yields the complete time evolution of the order parameter in the form of a Langevin equation. In particular, this equation provides the first closed-form steady-state distribution of the order parameter, fully capturing finite-size effects. Free from integrability constraints and the celebrated Ott-Antonsen ansatz, our theory shows excellent agreement with simulations across diverse coupling functions and frequency distributions, demonstrating broad applicability. Strikingly, it surpasses recent approaches near criticality and in the incoherent phase, where finite-size fluctuations are most pronounced.

We introduce helical twisted quadrilayer graphene (HTQG), four graphene sheets rotated by the same small angle, as a versatile and experimentally accessible platform for correlated topological matter. HTQG consists of three moiré lattices, formed by interference between adjacent graphene layers, that are twisted relative to each other. Lattice relaxation produces four types of large-scale commensurate domains. The domains are characterized by the stacking of the three moiré lattices and come in two types: Type-I "Bernal" stacking and Type-II "rhombohedral" stacking. Domain walls between adjacent stackings often host topologically protected edge states, forming networks at the supermoiré and super-supermoiré scales. Remarkably, all four moiré substructures have narrow bands at the same magic angle $\theta \approx 2.3^\circ$, allowing their correlated phases to be simultaneously targeted in device manufacturing. We argue that the Type-I domains are especially suitable for realizing robust superconductivity which emerges from doping topological insulators, and Chern insulators in $C = \pm 2$ bands.

We study modular theory in hyperfinite von Neumann algebras, i.e. in those of type II or type III, from the viewpoint of a subregion charge sector decomposition. We address this symmetry resolution by considering infinite tensor products of finite-dimensional algebras with fixed subregion charge values. An important ingredient is the combination of these algebras using direct integrals. This allows us to obtain the symmetry-resolved modular operator, modular flow, and modular correlation functions for hyperfinite algebras. Our approach establishes a mathematical foundation for recent results on symmetry resolution and modular theory in conformal field theory. Our analysis applies both to charges defined on a continuous range, or on a discrete set. The latter is of interest for condensed matter theory. Moreover, within the AdS/CFT correspondence we expect our findings to be relevant as a new ingredient for bulk spacetime reconstruction, including information from different boundary charge sectors.

Integrability is a cornerstone of classical mechanics, where it has a precise meaning. Extending this notion to quantum systems, however, remains subtle and unresolved. In particular, deciding whether a quantum Hamiltonian - viewed simply as a matrix - defines an integrable system is far from obvious, yet crucial for understanding non-equilibrium dynamics, spectral correlations, and correlation functions in many-body physics. We develop a statistical framework that approaches quantum integrability from a probabilistic standpoint. A key observation is that integrability requires a finite probability of vanishing energy gaps. Building on this, we propose a two-step protocol to distinguish integrable from non-integrable Hamiltonians. First, we apply a systematic Monte Carlo decimation of the spectrum, which exponentially compresses the Hilbert space and reveals whether level spacings approach Poisson statistics or remain mixed. The termination point of this decimation indicates the statistical character of the spectrum. Second, we analyze $k$-step gap distributions, which sharpen the distinction between Poisson and mixed statistics. Our procedure applies to Hamiltonians of any finite size, independent of whether their structure involves a few blocks or an exponentially fragmented Hilbert space. As a benchmark, we implement the protocol on quantum Hamiltonians built from the permutation group $\mathcal{S}_N$, demonstrating both its effectiveness and generality.

The family of nickelate superconductors have long been explored as analogs of the high temperature cuprates. Nonetheless, the recent discovery that certain stoichiometric nickelates superconduct up to high $T_c$ under pressure came as a surprise. The mechanisms underlying the superconducting state remain experimentally unclear. In addition to the practical challenges posed by working in a high pressure environment, typical samples exhibit anomalously weak diamagnetic responses, which have been conjectured to reflect inhomogeneous `filamentary' superconducting states. We perform wide-field, high-pressure, optically detected magnetic resonance spectroscopy to image the local diamagnetic responses of as grown La$_3$Ni$_2$O$_7$ samples in situ, using nitrogen vacancy quantum sensors embedded in the diamond anvil cell. These maps confirm significant inhomogeneity of the functional superconducting responses at the few micron scale. By spatially correlating the diamagnetic Meissner response with both the local tensorial stress environment, also imaged in situ, and stoichiometric composition, we unravel the dominant mechanisms suppressing and enhancing superconductivity. Our wide-field technique simultaneously provides a broad view of sample behavior and excellent local sensitivity, enabling the rapid construction of multi-parameter phase diagrams from the local structure-function correlations observed at the sub-micron pixel scale.

We use Pauli Path simulation to variationally obtain parametrized circuits for preparing ground states of various quantum many-body Hamiltonians. These include the quantum Ising model in one dimension, in two dimensions on square and heavy-hex lattices, and the Kitaev honeycomb model, all at system sizes of one hundred qubits or more – sizes at which generic quantum circuits are beyond the reach of exact state-vector simulation – thereby reaching utility scale. We benchmark the Pauli Path simulation results against exact ground-state energies when available, and against density-matrix renormalization group calculations otherwise, finding strong agreement. To further assess the quality of the variational states, we evaluate the magnetization in the x and z directions for the quantum Ising models and compute the topological entanglement entropy for the Kitaev honeycomb model. Finally, we prepare approximate ground states of the Kitaev honeycomb model with 48 qubits, in both the gapped and gapless regimes, on Quantinuum's System Model H2 quantum computer using parametrized circuits obtained from Pauli Path simulation. We achieve a relative energy error of approximately $5\%$ without error mitigation and demonstrate the braiding of Abelian anyons on the quantum device beyond fixed-point models.

In this paper we consider the phonons in monolayer graphene and we show the possibility for the spin-triplet superconducting excitations states by discretizing the single-particle excitations near Fermi wave vector. The molonayer graphene was supposed to be exposed under the influence of the external gate-potential and the local Coulomb interaction effects have been taken into account at each lattice site position in the monolayer. A sufficiently large temperature domain was found, where the superconducting order parameter is not vanishing. Corresponding to this, at the surprisingly high temperature limit, we obtain a narrow domain of the electron-phonon coupling parameter $\lambda_{\rm eff}$, emphasizing the superconducting state. We discuss the localizing role of Hubbard-$U$ interaction and the effects external gate potential on the calculated physical parameters in the system. We explain the importance of the chemical potential in the formation of the superconducting state. We show the existence of a large superconducting band-gap in the system even in the case of the absence of the applied electric field potential.

Due to their iconic linearly vanishing density of states near the zero-energy, half-filled two-dimensional Dirac materials in flat Euclidean and negatively-curved hyperbolic spaces exhibit dynamic mass generation only once a critical interaction strength is surpassed. Application of external magnetic fields onto these systems can, however, trigger the formation of such ordered phases yielding isotropic insulation near the band-center at arbitrarily weak coupling, a phenomenon known as magnetic catalysis. Recently, it has been proposed that a specific type of non-Hermiticity, allowing the system to feature an all-real eigenvalue spectrum otherwise squeezed toward the zero-energy, can bring down the requisite critical coupling of a specific family of ordered phases, commuting class masses, to a desired lower finite value in Dirac systems, a phenomenon known as non-Hermitian catalysis (arXiv:2501.18591). Here, we predict that a confluence of external magnetic fields and such a non-Hermiticity can amplify the magnitude of commuting class masses for subcritical strengths of interactions in Dirac liquids, an emergent phenomenon named non-Hermitian amplification of magnetic catalysis. We anchor this prediction from numerical self-consistent mean-field solutions of the commuting class mass charge-density-wave (antiferromagnetic) order displaying a staggered pattern of average electronic density (magnetization) between the nearest neighboring sites of the half-filled Euclidean honeycomb and hyperbolic {10, 3} and {14, 3} lattices, all featuring emergent non-Hermitian Dirac quasiparticles, after decomposing the nearest-neighbor Coulomb (on-site Hubbard) repulsion in the Hartree channel. We discuss the scaling behavior of these two orders with magnetic field and non-Hermiticity over a wide range of subcritical interactions.. Possible experimental setups to test our predictions are discussed.

Tensor network contraction on arbitrary graphs is a fundamental computational challenge with applications ranging from quantum simulation to error correction. While belief propagation (BP) provides a powerful approximation algorithm for this task, its accuracy limitations are poorly understood and systematic improvements remain elusive. Here, we develop a rigorous theoretical framework for BP in tensor networks, leveraging insights from statistical mechanics to devise a cluster expansion that systematically improves the BP approximation. We prove that the cluster expansion converges exponentially fast if an object called the loop contribution decays sufficiently fast with the loop size, giving a rigorous error bound on BP. We also provide a simple and efficient algorithm to compute the cluster expansion to arbitrary order. We demonstrate the efficacy of our method on the two-dimensional Ising model, where we find that our method significantly improves upon BP and existing corrective algorithms such as loop series expansion. Our work opens the door to a systematic theory of BP for tensor networks and its applications in decoding classical and quantum error-correcting codes and simulating quantum systems.

Charge order is a widely observed and representative example of spontaneous broken symmetries in quantum states of matter. Owing to the large intra-atomic Coulomb energy, the charge redistribution in such an order typically implies significant alteration of the electronic and lattice properties of materials. While the standard description of charge order, namely a "charge density wave" instability of the Fermi surface, has been broadly and successfully applied to good metals, its applicability to correlated ionic materials has been rather limited. Here, we propose an alternative general scenario of charge order - crystallization of long-lived Frenkel excitons - suitable for these ionic materials. We demonstrate this scenario on the recently discovered kagome superconductors and successfully reproduce all the characteristics of experimental observations on both local charge correlations and long-range ordering. The proposed generic scenario offers a long-sought understanding of charge order applicable to modern correlated functional materials.

We establish a relationship between the correlations in a many-qubit mixed state and the minimum circuit depth needed for its preparation. If the mutual information between two subsystems exceeds the mutual information between one of those subsystems and the environment, which purifies the mixed state of the system, then the past lightcones of the subsystems must intersect one another. This results in a lower bound on the circuit depth of any ensemble of geometrically local unitaries that prepares the state to some specified degree of approximation. As an application, we derive lower bounds on the circuit depth needed to prepare thermal states of one-dimensional quantum critical systems described by conformal field theory, showing that the depth diverges as temperature is decreased up to a cutoff set by the preparation error.

We studied the dynamics of a pair of single-electron double quantum dots (DQD) under longitudinal and transverse static magnetic fields and time-dependent harmonic modulation of their interaction couplings. We propose to modulate the tunnel coupling between the QDs to produce one-qubit gates and the exchange coupling between DQDs to generate entangling gates, the set of operations required for quantum computing. We developed analytical approximations to set the conditions to control the qubits and applied them to numerical calculations to test the accuracy and robustness of the analytical model. The results shows that the unitary evolution of the two-electron state performs the designed operations even under conditions shifted from the ideal ones.

Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to traditional physics-based modeling. However, scientific modeling requires physically meaningful predictions, raising a fundamental question for data-driven methods: to what extent can physical inductive biases-that is, prior assumptions about the structure of the physical world-emerge by learning from data alone? In this work, we study atomistic modeling, a representative field in the computational sciences where ML architectures have historically embedded strong physical inductive biases-such as geometric locality and graph structure. We develop a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions. We find that the model autonomously recovers key physical structure, such as learned interatomic interaction strengths that mirror classical electrostatics and interaction cutoffs consistent with traditional physical models. We further demonstrate predictable neural scaling law behavior with increased data and compute, and find accuracy on certain metrics competitive with physics-informed architectures. Our results clarify the boundary between engineered inductive biases and learnable physical structure, suggesting that general-purpose architectures can serve as principled baselines for scientific modeling by learning fundamental physical structure directly from data.

Many-body entanglement properties of quantum spin liquids (QSLs), persisting at arbitrarily long distances, have been intensely explored over the past two decades, but mostly for QSLs viewed as closed quantum systems. However, in experiments and potential quantum computing applications, candidate materials for this exotic phase of quantum matter will always interact with a dissipative environment, such as the one generated by bosonic quasiparticles in solids at finite temperature. Here we investigate both the stability and spatial distribution of entanglement for the Kitaev model of QSL, which is made open by its sudden coupling to an infinite bosonic bath of Caldeira-Leggett type and then time-evolved in both Markovian and non-Markovian regimes. From the time-dependent density matrix of QSL subregions, we extract genuine multipartite negativity (GMN), quantum Fisher information, spin-spin correlators, and the expectation value (EV) of the Wilson loop operator. In particular, time dependence of GMN offers the most penetrating insights: (i) in the Markovian regime, it remains nonzero only in hexagonal loopy subregions of QSL (as also discovered very recently for closed QSLs), eventually vanishing on the same timescale on which the EV of the Wilson loop operator vanishes; (ii) in the non-Markovian regime with pronounced memory effects, surprisingly, GMN remains nonzero up to much higher temperatures while also remaining zero in non-loopy subregions. In addition, the non-Markovian dynamics generates emergent interactions between spins, thereby opening avenues for tailoring properties of QSL via engineering of dissipation.

Rydberg-cavity systems are emerging as promising platforms for quantum simulation and quantum information processing. These hybrid architectures combine two complementary interaction mechanisms: cavity photons mediate collective long-range couplings, while Rydberg excitations generate strong short-range interactions. Together, they offer a setting for engineering many-body phases characterized by a hierarchy of interactions across widely different length scales. In this work, we introduce a minimal and scalable model for such systems. Focusing on the strong Rydberg blockade regime, we restrict the Hilbert space to the subspace enforced by the blockade, yielding a kinetically constrained long-range model in one spatial dimension. This approach both captures the physics of Rydberg-cavity experiments in the regime of strong Rydberg interactions and provides a conceptually transparent framework for studying the interplay of long-range and short-range interactions. At equilibrium, in addition to paramagnetic and Néel-ordered phases, the system supports a blockaded ferromagnetic/superradiant phase, distinct from the conventional superradiant phase. Out of equilibrium, we identify long-range quantum many-body scars, which are atypical nonthermal eigenstates that evade the eigenstate thermalization hypothesis, and giving rise to slow entanglement growth. In contrast to the linear-in-time entanglement growth characteristic of short-range scarred models, these long-range scars exhibit logarithmic entanglement dynamics. Our results establish a minimal yet versatile framework for Rydberg-cavity systems, and provide a stepping stone for future theoretical and experimental studies of this frontier platform in quantum many-body physics.

The V2O3 phase diagram contains two insulating phases and one metallic phase with different lattice structures. The stability of these phases is very sensitive to pressure, offering a mechanism to tune phase transitions by inducing strain in thin films. The most studied source of strain is lattice mismatch between the film and the substrate. In this work, however, we find that the film/substrate thermal expansion mismatch can be made to play a dominant role by modifying the substrate morphology. When grown on sapphire, the lattice mismatch induces compressive strain in the V2O3 films, whereas thermal expansion mismatch induces tensile strain. We find that minute changes in substrate morphology may relax the compressive strain component, allowing the thermally-induced tensile component to overcome it. Thus, by simple annealing of the substrates to create either a flat or stepped morphology, strongly compressive or tensile strains may be induced in the films. This results in either full suppression of the metal-insulator transition or stabilization of insulating phases at all temperatures, exhibiting many orders of magnitude differences in film resistivity. To elucidate the strain relaxation mechanism, we use high-resolution scanning transmission electron microscopy (HRSTEM) to image the atomic steps in the substrate and the adjacent crystallographic defects in the V2O3. These findings offer a hitherto underexplored mechanism to tune strain in thin films, deepen our understanding of the effects of structural degrees of freedom on phase stability of a canonical Mott insulator and may allow for applications requiring insulator-metal switching above room temperature.

Organic Quantum Chains (OQCs) represent a newly synthesized class of carbon-based nanostructures whose quasi-one-dimensional nature gives rise to unconventional electronic and transport phenomena. Here we investigate the electronic and transport properties of recently synthesized OQCs [Nature Communications, 12, 5895 (2021)]. Structural stability was first assessed through molecular dynamics relaxation combined with density functional theory (DFT). The optimized coordinates are then used in a tight-binding model with exponentially decaying hopping parameterization, which reproduces the DFT results with high accuracy. Our calculations reveal a robust and nearly constant energy gap across several OQC configurations, in agreement with experimental data. We also identify emergent hierarchical states, characterized by distinct localization behaviors within sets of localized bands. Finally, we analyze different transport responses in scenarios involving the one-dimensional OQC coupled to carbon corrals, as observed in the experimental data, highlighting their potential as promising systems for application in carbon nanodevices.

Selectively exciting and manipulating phonons at nanoscale becomes more and more important but still remains challenging in modern nano-energy control and information sensing. Here, we show that the phonon spin angular momentum provides an extra degree of freedom to achieve versatile manipulation of axial phonons in nanomaterials via coupling to spinful multi-physical fields, such as circularly polarized infrared absorption. In particular, we demonstrate the nanoscale one-way axial phonon excitation and routing in chiral nanomaterials, by converting the photon spin in circularly polarized optical fields into the collective interference phonon spin. As exemplified in the smallest chiral carbon nanotube, we show that the rectification rate can reach nearly 100%, achieving an ideal one-way phonon router, which is verified by molecular dynamics simulations. Our results shed new light on the flexible phonon manipulation via phonon spin degree of freedom, paving the way for future spin phononics.

Quantum generalizations of the Fisher information are important in quantum information science, with applications in high energy and condensed matter physics and in quantum estimation theory, machine learning, and optimization. One can derive a quantum generalization of the Fisher information matrix in a natural way as the Hessian matrix arising in a Taylor expansion of a smooth divergence. Such an approach is appealing for quantum information theorists, given the ubiquity of divergences in quantum information theory. In contrast to the classical case, there is not a unique quantum generalization of the Fisher information matrix, similar to how there is not a unique quantum generalization of the relative entropy or the Rényi relative entropy. In this paper, I derive information matrices arising from the log-Euclidean, $\alpha$-$z$, and geometric Rényi relative entropies, with the main technical tool for doing so being the method of divided differences for calculating matrix derivatives. Interestingly, for all non-negative values of the Rényi parameter $\alpha$, the log-Euclidean Rényi relative entropy leads to the Kubo-Mori information matrix, and the geometric Rényi relative entropy leads to the right-logarithmic derivative Fisher information matrix. Thus, the resulting information matrices obey the data-processing inequality for all non-negative values of the Rényi parameter $\alpha$ even though the original quantities do not. Additionally, I derive and establish basic properties of $\alpha$-$z$ information matrices resulting from the $\alpha$-$z$ Rényi relative entropies. For parameterized thermal states and time-evolved states, I establish formulas for their $\alpha$-$z$ information matrices and hybrid quantum-classical algorithms for estimating them, with applications in quantum Boltzmann machine learning.

We study chaotic many-body quantum dynamics in a minimal model with spatial structure and local interactions. It has a time-independent Hamiltonian, in contrast to quantum circuits and Brownian models, and is simple at the single-site level, in contrast to Sachdev-Ye-Kitaev chains. It is analytically tractable for large local Hilbert space dimension and weak intersite coupling. In this limit we show that energy dynamics is described by a classical master equation and is diffusive. We also show that the spectral form factor can be expressed exactly in terms of the solution to this master equation. For a two-site system we obtain closed-form expressions for both the two-point correlator of energy density and the spectral form factor, in essentially perfect agreement with numerical simulations. For an $L$-site system we show at late times how a linear ramp emerges in the spectral form factor, as universally expected from level repulsion in chaotic quantum systems. Conversely, at earlier times we identify two distinct mechanisms for an increase of the spectral form factor above its ramp value. One of these is associated with energy diffusion and is effective until the Thouless time, which varies as $L^2$. The other involves contributions like those that would appear if the system were composed of many uncoupled subsystems: they generate a large enhancement of the spectral form factor, and are suppressed on a timescale varying as $(\ln L)^2$. Besides being exact for the limit considered, we believe our approach provides the natural approximation even for small local Hilbert space dimension and strong intersite coupling. We present a numerical study of a spin-half chain, finding an early-time enhancement of the spectral form factor which is qualitatively similar to that in our solvable model.

Topological defects provide a unifying language to describe how orientational order breaks down in active and living matter. Considering cells as elongated particles confluent, epithelial tissues can be interpreted as nematic fields and its defects have been linked to extrusion, migration, and morphogenetic transformations. Yet, epithelial cells are not restricted to nematic order: their irregular shapes can express higher rotational symmetries, giving rise to p-atic order. Here we introduce a framework to extract p-atic fields and their defects directly from experimental images. Applying this method to MDCK cells, we find that all symmetries generate defects.No strong positional or orientational correlations are found between nematic and hexatic defects, suggesting that different symmetries coexist largely independently. These results demonstrate that epithelial tissues should not be described by nematic order alone, but instead host a spectrum of p-atic symmetries. Our work provides the first direct experimental evidence for this multivalency of order and offers a route to test and refine emerging p-atic liquid crystal theories of living matter.

Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge. We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.

The Hubbard model has occupied the minds of condensed matter physicists for most part of the last century. This model provides insight into a range of phenomena in correlated electron systems. We wish to examine the paradigm of quantum algorithms for solving such many-body problems. The focus of our current work is on the one-dimensional model which is integrable, meaning that there exist analytical results for determining its ground state. In particular, we demonstrate how to perform a gate-based quantum computer simulation of quantum annealing for the Hubbard Hamiltonian. We perform simulations for systems with up to 40 qubits to study the scaling of required annealing time for obtaining the ground state. We find that for the half-filled cases considered, there is a substantial quantum speed-up over algorithms based on the Bethe-ansatz equations.

Effective hydrodynamic modeling is crucial for accurately predicting fluid-particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures like GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne-Prager-Yamakawa (RPY) level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.

Experimental and theoretical studies of the high-pressure phases of hydrogen are highly sensitive to methodological choices. We compare the cold phase diagram of hydrogen calculated between 400-700 GPa with meta-GGA functionals (R2SCAN and SCAN0) with the commonly employed GGA-PBE functional. Molecular phases are stabilized over the atomic phase to higher pressures with the meta-GGA, in closer agreement with diffusion Monte Carlo calculations. R2SCAN phonon spectra show that the dynamical instabilities and anharmonic signatures predicted at the GGA level vanish, indicating that such effects may partly arise from functional deficiencies rather than quantum nuclear effects. Bonding analysis reveals that PBE artificially weakens intramolecular H-H bonds and enhances intermolecular interactions through charge delocalization, whereas meta-GGA preserves a more localized molecular character.

Correlated structures are intimately connected to intriguing phenomena exhibited by a variety of disordered systems such as soft colloidal gels, bio-polymer networks and colloidal suspensions near a shear jamming transition. The universal critical behavior of these systems near the onset of rigidity is often described by traditional approaches as the coherent potential approximation - a versatile version of effective-medium theory that nevertheless have hitherto lacked key ingredients to describe disorder spatial correlations. Here we propose a multi-purpose generalization of the coherent potential approximation to describe the mechanical behavior of elastic networks with spatially-correlated disorder. We apply our theory to a simple rigidity-percolation model for colloidal gels and study the effects of correlations in both the critical point and the overall scaling behavior. We find that although the presence of spatial correlations (mimicking attractive interactions of gels) shifts the critical packing fraction to lower values, suggesting sub-isostatic behavior, the critical coordination number of the associated network remains isostatic. More importantly, we discuss how our theory can be employed to describe a large variety of systems with spatially-correlated disorder.

We apply two ab initio many-body methods based on Gutzwiller wave functions, i.e., correlation matrix renormalization theory (CMRT) and Gutzwiller conjugate gradient minimization (GCGM), to the study of crystalline phases of atomic hydrogen. Both methods avoid empirical Hubbard U parameters and are free from double-counting issues. CMRT employs a Gutzwiller-type approximation that enables efficient calculations, while GCGM goes beyond this approximation to achieve higher accuracy at higher computational cost. By benchmarking against available quantum Monte Carlo results, we demonstrate that while both methods are more accurate than the widely used density-functional theory (DFT), GCGM systematically captures additional correlation energy missing in CMRT, leading to significantly improved total energy predictions. We also show that by including the correlation energy $E_c$ from LDA in the CMRT calculation, CMRT+$E_c$ produces energy in better agreement with the QMC results in these hydrogen lattice systems.

Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-trained phase network. We introduce a modified stochastic reconfiguration method that effectively uses differing imaginary time steps to evolve the amplitude and phase. Using a larger time step for phase optimization, this method enables a simultaneous and efficient training of phase and amplitude neural networks. The efficacy of our method is demonstrated on the Heisenberg J_1-J_2 model.