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The development of experimental techniques at the nanoscale has enabled the performance of spectroscopic measurements on single-molecule current-carrying junctions. These experiments serve as a natural intersection for the research fields of optical spectroscopy and molecular electronics. We present a pedagogical comparison between the perturbation theory expansion of standard nonlinear optical spectroscopy and the (non-self-consistent) perturbative diagrammatic formulation of the nonequilibrium Green’s functions method (which is widely used in molecular electronics), highlighting their similarities and differences. By comparing the two approaches, we argue that the optical spectroscopy of open quantum systems must be analyzed within the more general Green’s function framework. 

We study entanglement created between two isolated qubits by interaction with entangled-photon pairs obtained by parametric down-conversion of a laser pump field. The induced entanglement is quantified using the mixed state Concurrence proposed by Wootters et al. [Phys. Rev. Lett. 78, 5022 (1997)]. A universal value of qubit-entanglement, which is independent on the photon-pair wavefunction is identified to leading order in the qubit–field interaction and the pump field amplitude. The qubit entanglement decreases at higher laser pump intensities due to interference between the entangled photon pairs, which creates excitations in the qubit system. Maximal Concurrence is produced by only generating coherences between the ground and the highest excited qubit states. 

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a “spectrum-structure” correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.