The Road to and from Quantum Entanglement Experiments

M. M. Zhang, Jiahao Fan, Frank Schlawin +1 more

Quantum entanglement offers an incredible resource for quantum-enhanced sensing, imaging, and spectroscopy. Here we evaluate the enhancement of stimulated Raman scattering (SRS) signal by entangled photons. We demonstrate that the time-energy correlation of the photon pairs can optimize the signal for polyatomic molecules. Our results show that the spectral-line intensity of the entangled-photon SRS (ESRS) is of the same order of magnitude as the one for the entangled two-photon absorption (ETPA); the parameter window is thus identified to do so. Moreover, the vibrational coherence is found to play an important role for enhancing the ESRS against the ETPA intensity. Our work paves a firm road for extending the schemes of molecular spectroscopy with quantum light, based on the observation of the ETPA in experiments.

F.J. Duarte

Mingran Zhang, Jiahao Joel Fan, Frank Schlawin +1 more

Quantum entanglement offers an incredible resource for enhancing the sensing and spectroscopic probes. Here we develop a microscopic theory for the stimulated Raman scattering (SRS) using entangled photons. We demonstrate that the time-energy correlation of the photon pairs can optimize the signal for polyatomic molecules. Our results show that the spectral-line intensity of the entangled-photon SRS (ESRS) is of the same order of magnitude as the one for the entangled two-photon absorption (ETPA); the parameter window is thus identified to do so. Moreover, the vibrational coherence is found to play an important role for enhancing the ESRS against the ETPA intensity. Our work paves a firm road for extending the schemes of molecular spectroscopy with quantum light, based on the observation of the ETPA in experiments.

F. J. Duarte

Haohua Zheng, Heying Li, Jianchen Zhang +3 more

With advancements in neural networks, intelligent road network selection methods have become a key research area. However, the expanding scale of road networks has led to concerns regarding model training efficiency and resource consumption. Quantum neural networks, leveraging their unique properties of superposition and entanglement, present remarkable advantages for handling large-scale, complex, and nonlinear data. We propose a novel framework for road network selection based on quantum neural networks. We design a comprehensive feature set that accounts for various factors, including terrain, settlements, and surrounding density. Our study delves into the impact of feature encoding methods and circuit structures on the performance of quantum neural networks in road selection. It evaluates the proposed model’s performance across different scales, regions, and data volumes. The results demonstrate the feasibility and effectiveness of our approach when compared to existing classical neural network models, offering a promising solution for large-scale road network selection.