Harsh Bhakta | Searcharxiv

arXiv2026

Multidimensional semiclassical single- and double-quantum spectroscopy of anharmonic molecular polaritons

We present a general and efficient approach to compute phase-resolved multidimensional spectra of anharmonic molecular polaritons, based on a semiclassical evolution of the molecular Hamiltonian and cavity field in the large-$\mathcal{N}$ limit of many molecules coupled to a confined photonic mode. By systematically expanding the response in both amplitudes and phases of the input fields, our method enables a transparent and computationally simple construction of phase-cycled two-dimensional single- and double-quantum polariton spectra from the underlying nonlinear signal components. Here, phase cycling acts as an analogue of phase matching with oblique pulses, allowing for the isolation of the contributing nonlinear pathways in Liouville space. We specialize to vibrational polaritons and benchmark the method through direct comparison with experimentally measured single-quantum spectra, providing an explanation for the longstanding puzzle of the polariton bleach effect observed at short waiting times. Further, we show how the imprint of various types of anharmonicities on the double-excitation manifold can be directly probed and analyzed through double-quantum coherence spectroscopy. Taken together, our results establish a practical and powerful framework for the modeling and interpretation of nonlinear spectroscopic experiments on strongly coupled light-matter platforms and for guiding the design of cavity-enhanced molecular platforms.

quant-ph↗

arXiv2026

Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling

High-dimensional hyperspectral imaging (HSI) enables the visualization of ultrafast molecular dynamics and complex, heterogeneous spectra. However, applying this capability to resolve spatially varying vibrational couplings in two-dimensional infrared (2DIR) spectroscopy, a type of coherent multidimensional spectroscopy (CMDS), necessitates prohibitively long data acquisition, driven by dense Nyquist sampling requirements and the need for extensive signal accumulation. To address this challenge, we introduce a physics-informed neural representation approach that efficiently reconstructs dense spatially-resolved 2DIR hyperspectral images from sparse experimental measurements. In particular, we used a multilayer perceptron (MLP) to model the relationship between the sub-sampled 4D coordinates and their corresponding spectral intensities, and recover densely sampled 4D spectra from limited observations. The reconstruction results demonstrate that our method, using a fraction of the samples, faithfully recovers both oscillatory and non-oscillatory spectral dynamics in experimental measurement. Moreover, we develop a loss-aware adaptive sampling method to progressively introduce potentially informative samples for iterative data collection while conducting experiments. Experimental results show that the proposed approach achieves high-fidelity spectral recovery using only $1/32$ of the sampling budget, as opposed to exhaustive sampling, effectively reducing total experiment time by up to 32-fold. This framework offers a scalable solution for accelerating any experiments with hypercube data, including multidimensional spectroscopy and hyperspectral imaging, paving the way for rapid chemical imaging of transient biological and material systems.

eess.IV↗