Simulated and Phantom Detection of Microscopic Targets by Raman Spectroscopy

The goal of this study is to determine the minimum spatial sampling resolution required to accurately detect microscopic targets within a sample using Raman spectroscopy. The resolution depends on the light scattering properties of the material. We use Monte Carlo simulations to study how measurement geometry and optical properties of a sample affect the Raman signal detected from an embedded target. We confirm these results using polystyrene beads embedded in artificial tissue phantoms

Witnessing Light-Driven Entanglement using Time-Resolved Resonant Inelastic X-Ray Scattering

Characterizing and controlling entanglement in quantum materials is crucial for next-generation quantum technologies. However, defining a quantifiable figure of merit for entanglement in a material is theoretically and experimentally challenging. At equilibrium, the presence of entanglement can be diagnosed by extracting entanglement witnesses from spectroscopies and extending this approach to nonequilibrium states could lead to the discovery of novel dynamical phenomena. Here, we propose a systematic approach to quantify the time-dependent quantum Fisher information and entanglement depth of transient states of quantum materials through time-resolved resonant inelastic x-ray scattering, a recently developed solid-state pump-probe technique. Using a quarter-filled extended Hubbard model as an example, we benchmark the efficiency of this approach and predict a light-enhanced quantum entanglement, due to the proximity to a phase boundary. Our work sets the stage for experimentally witnessing and controlling entanglement in light-driven quantum materials via solid-state accessible ultrafast spectroscopic measurements.Comment: 11 pages, 6 figure

Strain-induced enhancement of TcT_c in infinite-layer Pr0.8_{0.8}Sr0.2_{0.2}NiO2_2 films

The mechanism of unconventional superconductivity in correlated materials remains a great challenge in condensed matter physics. The recent discovery of superconductivity in infinite-layer nickelates, as analog to high-Tc cuprates, has opened a new route to tackle this challenge. By growing 8 nm Pr0.8Sr0.2NiO2 films on the (LaAlO3)0.3(Sr2AlTaO6)0.7 substrate, we successfully raise the transition temperature Tc from 9 K in the widely studied SrTiO3-substrated nickelates into 15 K. By combining x-ray absorption spectroscopy with the first-principles and many-body simulations, we find a positive correlation between Tc and the pre-edge peak intensity, which can be attributed to the hybridization between Ni and O orbitals induced by the strain. Our result suggests that structural engineering can further enhance unconventional superconductivity, and the charge-transfer property plays a crucial role in the pairing strength.Comment: 8 pages, 4 figure

A Novel Hybrid Quantum-Classical Framework for an In-Vehicle Controller Area Network Intrusion Detection

In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. An attacker can inject false messages to a vehicle’s CAN via wireless communication, the infotainment system, or the onboard diagnostic port. Thus, an effective intrusion detection system is essential to distinguish authentic CAN messages from false ones. In this study, we developed a hybrid quantum-classical CAN intrusion detection framework using a classical neural network (NN) and a quantum restricted Boltzmann machine (RBM). The classical NN is dedicated to feature extraction from CAN images generated from a vehicle’s CAN bus data. In contrast, the quantum RBM is dedicated to CAN image reconstruction for classification-based intrusion detection. The novelty of the study lies in utilizing the generative ability of an RBM to reconstruct the pixels in a CAN image, a portion of which is dedicated to labeling. Then, that portion of the reconstructed image is used to classify the image as an attack image or a normal image. To evaluate the performance of the hybrid quantum-classical CAN intrusion detection framework, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid framework to a similar but classical-only framework. Our analyses showed that the hybrid framework performs better in CAN intrusion detection compared to the classical-only framework. For the three datasets considered in this study, the best models in the hybrid framework achieved 97.5%, 97%, and 98.3% intrusion detection accuracies and 94.7%, 93.9%, and 97.2% recalls, respectively. In contrast, the best models in the classical-only framework achieved 92.5%, 95%, and 93.3% intrusion detection accuracies and 84.2%, 89.8%, and 88.9% recalls, respectively