858 papers
This paper establishes a rigorous geometric algebra framework that utilizes bivectors and the Spin(3) group to resolve kinematic singularities, enabling the classification of orbits in galactic potentials through a topologically stable Rotor-Bivector Atlas and an efficient algebraic frequency extraction algorithm.
This paper proposes a compact neuromorphic optical computing architecture that encodes trainable transformations within the multipolar response of a single Mie scatterer, demonstrating that such a device can achieve classification performance comparable to a single-layer neural network while overcoming the parameter-density limitations of conventional diffractive processors.
This paper proposes a systematic audit to distinguish between direct cosmological measurements and their model-dependent interpretations, demonstrating that parameters like dark energy fractions and singularities are conditional estimates rather than direct observations, thereby establishing a reproducible framework for clarifying the evidential status and limitations of cosmological claims.
This paper proposes a novel hydrodynamic framework for macroscopic gravity that models the gravitational field as a vacuum fluctuation energy flow using quaternion tensors and viscous dissipation to achieve thermodynamic equilibrium, thereby resolving topological singularities and preventing infinite density collapse without relying on quantum gravity.
This paper presents an exactly solvable two-qubit model demonstrating that apparent local energy nonconservation in Everettian branching is an observational artifact of subsystem confinement, rigorously proven to be perfectly counterbalanced by time-dependent shifts in branch probabilities to ensure strict global energy conservation.
This paper demonstrates a quantum measurement framework that enables single-photon object identification without imaging by applying task-optimized unitary transformations to engineer a measurement basis where wavefunction collapse directly reveals the object label, thereby achieving significantly higher accuracy than classical intensity-based methods under low-photon conditions.
This paper demonstrates that the volumetric viscous dissipation of a vibrating sessile drop can be accurately estimated solely from its external silhouette deformation, enabling the prediction of internal energy transfer and dissipation across a wide range of viscosities without requiring optical access to the drop's interior.
This paper presents a dual validation framework combining extensive simulations and experimental runs on the 156-qubit IBM Quantum backend to demonstrate that quantum discord remains a robust and predictive diagnostic resource for noisy intermediate-scale quantum devices, effectively bridging theoretical models with hardware realities.
PatternFormer is a large language model-based framework that overcomes the single-valued limitations of existing neural surrogates by transforming unordered coexisting solutions into canonical sequences, enabling the autoregressive generation of complete solution sets for nonlinear reaction-diffusion systems, including physically valid states beyond the training data.
This paper presents a unified stochastic drift model for superconducting charge qubits based on memory multi-fractional Brownian motion and a time-dependent quantum extension, which accurately captures non-stationary 1/f noise and long-range correlations to predict coherence times and non-Markovian decay patterns that surpass conventional Markovian approaches.