Xi-Jun Yuan | Searcharxiv

arXiv2026

Periodic conditional measurements on a photonic chip

Quantum measurements not only extract information but also reshape quantum evolution through measurement backaction. Here, we investigate periodic conditional measurements, in which coherent evolution is repeatedly interrupted by local measurements. We show that the bright and dark modes of the conditional evolution operator provide a unified spectral description of both the post-selected final state along no-detection trajectories and the statistics of detection events. Using a three-dimensional photonic chip, we demonstrate on-demand state engineering through dark-mode preservation and bright-mode selection, enabling the protection of topological corner states and excited-state shelving. We further observe the predicted transition of the total detection probability on an effectively infinite one-dimensional lattice and identify its microscopic origin in Floquet near-dark modes. These results establish periodic conditional measurements as a general framework for engineering conditional quantum states and manipulating event-detection statistics in monitored quantum dynamics.

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arXiv2022

Quantum support vector machines for aerodynamic classification

Aerodynamics plays an important role in aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on airfoil is critical for ensuring stable and efficient aviation. However, since it is challenging to understand the mechanics of flow field separation, the aerodynamic parameters are emphasized for the identification and control of flow separation. It has been investigated extensively using traditional algorithms and machine learning methods such as the support vector machine (SVM) models. Recently, a growing interest in quantum computing and its applications among wide research communities sheds light upon the use of quantum techniques to solve aerodynamic problems. In this paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing model, to identify whether there is flow separation, with their performance in comparison to the widely-used classical SVM. We show that our approach outperforms the classical SVM with an 11.1% increase of the accuracy, from 0.818 to 0.909, for this binary classification task. We further develop multi-class qSVMs based on one-against-all algorithm. We apply it to classify multiple types of the attack angles to the wings, where the advantage over the classical multi-class counterpart is maintained with an accuracy increased from 0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for classifying flow separation scenarios, and may promote rich investigations for quantum computing applications in fluid dynamics.

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