arXiv preprints from January 1, 2026 through September 8, 2026 — 16:20:18 EST
In this paper we investigate the behavior of localized solutions, specifically solitons, in a system of three coupled waveguides. The nonlinearity is modeled by a quasi-periodic modulation influencing the interaction between the waveguides. We analyze the evolution of the soliton profiles and their dynamics under varying modulation...
Unlike second-order phase transitions, a first-order transition has a stage, in which a system is trapped in a metastable state. The decay of this state leads to abundant excitations over the ground state. We present a field theory for kinetics of defects emerging during quantum annealing computations. This theory predicts several...
We develop a formalism for extracting relativistic classical dynamics from the S-matrix, in which massive bodies are described on phase space while massless radiation is kept in the Fock space. Our formalism relies on the Stratonovich-Weyl (SW) correspondence between the operator and phase-space formulations of quantum mechanics, as...
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints....
The observed diversity of galaxy centers motivates the possibility that the local dark matter composition may vary among galaxies. In conventional multicomponent dark matter scenarios, however, large stochastic variations in the relative abundances are not generically expected, with the cold component typically remaining dominant. We...
Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to...
Quantum state ensembles are important in quantum information processing. For example, quantum $t$-designs model highly entangled states in complex systems, while projected ensembles appear in generative quantum machine learning and studies of thermalization. With their sample state accompanied by a classical label, these ensembles...
We present a multiwavelength analysis of the radio transient VT J1906+0849, discovered as a 70 mJy source in Epoch 1 of the Very Large Array Sky Survey (VLASS), 21 yr after an NRAO VLA Sky Survey (NVSS) non-detection. Radio observations reveal the source was first detected in 2005, peaking at $\gtrsim200$ mJy in 2014, then declining...
We develop a phase-space description of classical scattering in which the matter sector of the Dyson S-matrix is partially Weyl transformed while the radiation sector remains operator valued. The resulting S-matrix symbol provides a common origin for several classical observables: we show that the inclusive waveform, the classical...
We realise the first ultralow-field Bose-Einstein condensation of triplons in a spin-ladder magnet, uncovering a quantum critical point at only $μ_0 H_{c1}=0.17$ T in Henmilite ($\mathrm{Ca_2Cu(OH)_4[B(OH)_4]_2}$). Unlike dimer magnets, a ladder retains extended one-dimensional correlations in its gapped parent state, making this...
Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a...
The Petz recovery map is considered one of the key candidates for the quantum analogue of Bayesian inference and Jeffrey's conditionalization. Since, there seems to be a natural connection between Bayesian inference and the notion of state tomography, it is natural to ask if the Petz recovery can be used for this latter task. In this...
We prove that for the spin-1/2 Heisenberg XXZ chain in the range $Δ\in[-1,1/2]$, the ground state on the torus of length $L$ converges to an infinite volume ground state $\langle\cdot\rangle$ as $L\to\infty$, and that the spin-spin correlation $\langle S_0^{(1)}S_x^{(1)}\rangle$ decays polynomially fast in $x$. In the range...
These notes accompany lectures given in June 2025 at the summer school \emph{Fundamental Problems in Statistical Physics XVI}. They offer a short introduction to random quantum circuits as simple models for generic many-body quantum systems. They give an outline of the motivation for introducing these models, starting from ideas of...
We investigate chemical control of the electronic, optical, and topological properties of two-dimensional \ce{Si2X2Te2} (\ce{X} = \ce{B}, \ce{Al}, \ce{Ga}, and \ce{In}) monolayers using first-principles calculations. All compounds are dynamically stable semiconductors, with their vibrational and electronic properties evolving...
We constrain the large-scale isotropic random component of the Galactic magnetic field using the Planck reconstruction of the 408 MHz synchrotron sky. Our analysis simultaneously fits the random-field structure, the synchrotron contributions of the coherent field and local foregrounds, and isotropic and dipolar offsets. A new element...
Metabolic networks play pivotal roles in understanding the evolution of organisms, microbiome dynamics and disease prevention and treatment. This study presents a comprehensive analysis of metabolic network properties across 10912 organisms spanning Bacteria, Archaea, and Eukarya domains. A novel method for the network construction is...
Models incorporating household structure, with different rates of transmission within and between households, are widely used in infectious disease epidemiology. These models can be calibrated using final-size data in which transmission ordering is ignored because it does not affect the distribution of final outbreak sizes. In...
Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Transport (OT) is popular, existing frameworks are largely restricted to first-order dynamics, assuming memoryless velocity fields. This limits expressiveness, as first-order systems fail...
The scientific study of consciousness frequently stalls on ontological debates regarding the "Hard Problem." This paper proposes a pragmatic pivot. Rather than asking what consciousness is metaphysically, we ask how modeling conscious access as a specific computational transformation may address existing bottlenecks in neuroscience...
Gapped k-mer support vector machines (gkm-SVMs) are widely used for classifying regulatory DNA sequences and identifying the sequence features underlying those predictions. Although LS-GKM provides an efficient implementation of gkm-based kernels, it is restricted to binary classification and does not provide calibrated probability...
Short observational pricing panels can contain many observations while offering only a small number of distinct price movements. This paper studies the inferential consequences of that distinction in a synthetic data-generating process calibrated to a sparse pricing regime. We separate uncertainty conditional on a realised price...
We study an experiment with 8.5 million users on Netflix's recommender system to measure how improvements in recommendation technology affect the set of products that get consumed. Improvements increase total consumption and users' reliance on recommendations while diffusing recommendations and consumption away from the most popular...
Marketing Mix Models (MMMs) estimate the incremental sales effect of advertising from observational time series, yet they are rarely validated against ground truth, because ground truth is unobservable in real data. Synthetic data closes that gap in principle, but existing generators produce marketing spend exogenously - omitting the...
Marketing Mix Models (MMMs) are widely used for marketing measurement and budget allocation, but face fundamental identification challenges: due to endogenous marketing spend decisions, MMM estimation on observational time-series data cannot recover the true causal effects of marketing. On the other hand, geo-experiments provide...