Quantum Tech

Quantum/Chemistry

A spectral co-design framework that directly couples fabrication-constrained SPSA-Adam optimizer with full-wave finite-element eigenmode simulations for multifrequency Rydberg electrometry, operating without analytical or adjoint gradients.

Quantum/Chemical

This paper presents a privacy-preserving hybrid quantum-classical computing framework that integrates parameterized quantum circuits with classical components, targeting quantum computing proxy applications such as chemistry simulation.

Quantum/Chemical

For quantum many-body physics and ab initio quantum chemistry, this work derives an unbiased direct gradient estimator and introduces the adaptive minimum-variance phase (AMVP) estimator to overcome training failures in weak-gradient regimes for neural-network variational optimization.

Materials

Presents the first complete machine learning method for accelerating plane-wave DFT in materials under the PAW formalism, formalizing seven criteria that a Complete Neural Electronic Initializer must satisfy and addressing two missing structure-dependent components: augmentation occupancies and spin initialization.

Quantum/Chemistry

A physics-informed dual-state query learning framework for GPCR ligand bioactivity prediction, addressing the neglect of dynamic conformational equilibria in existing deep learning approaches to improve functional efficacy discrimination.

Quantum/Chemistry

Proposes finite-state automata encoding recursive lattice geometry for exact MPO construction of tight-binding Hamiltonians across fractals, quasicrystals, and hyperbolic lattices.

Quantum / Chemistry

A probabilistic physics-informed framework using WaveNet and ParamNet to solve the inverse problem of recovering Woods-Saxon nuclear potential parameters from sparse spectral data.

Materials

Hybrid quantum neural network extracts quantitative parameters like phase weight fractions directly from 1D powder diffraction patterns without iterative refinement, suitable for complex multiphase materials and noisy measurements.

Quantum

Using physics-informed neural networks to solve electron spin dynamics in few-cycle laser pulses, deriving analytical expressions for the polarization angle of electrons initially at rest in CEP-stabilized linearly polarized plane-wave pulses.

Electromagnetics

Proposes Neural Modal Decomposition (NMD) to decompose multi-port system response matrices into pole-residue form for neural network-based modeling of RF cavities and photonic devices.

Quantum/Chemical

Proposes iCRNNs to discover chemical reaction network structures and kinetics from time-series concentration data without prior mechanistic assumptions, balancing interpretability and computational efficiency.

Quantum/Chemical

MARC-TS introduces a two-stage framework that learns endpoint-conditioned continuous reaction paths, queries at arbitrary resolution, and refines transition-state candidates via local path context, with T1x-IRC-8K dataset of 8,209 reactions and 1.09M path-resolved geometries.

Electromagnetics

Presents a general method for producing stochastic synthetic axion waveforms by shaping Gaussian white noise in the frequency domain, targeting microwave cavity haloscope calibration and analysis pipeline validation for dark matter axion searches.