Principle of Entangled-Photon Thermometry for Ultrafast Laser Processing
A quantum-enhanced approach for fast temperature diagnostics in ultrashort laser surface processing is introduced. The goal is to overcome limitations of existing methods, such as plasma emission, emissivity changes during ablation, and the need for time-consuming pump-probe measurements. The proposed method exploits polarization anisotropy in entangled photon pairs. The idler photon interacts with the laser-affected material surface, while its entangled counterpart is detected in a remote optical arm. Temperature-dependent changes in the complex refractive index modify the reflectance of p- and s-polarizations on the idler path, altering the coincidence-resolved polarization statistics of the signal photons. Using a Qiskit-based model incorporating experimental pump-probe reflectometry data, remote reconstruction of rapid thermal dynamics during femtosecond laser ablation is demonstrated. Although based on simulation, the model employs literature data to represent realistic material behavior. Due to the discrete nature of single-photon events, classical sliding-window analysis suffers from shot noise and temporal inertia. To overcome this limitation, a multilayer perceptron (MLP) regression network is applied to extract implicit anisotropy information from the photon bitstream. Compared with the classical approach, the neural-network method improves reconstruction robustness, reduces temperature noise, enhances the signal-to-noise ratio (SNR), and enables nanosecond-scale tracking of thermal dynamics. The results indicate that entangled-photon polarization anisotropy combined with machine-learning analysis is a promising approach for remote, background-rejected, high-speed temperature diagnostics in laser-matter interaction studies.