data i miejsce: 11.06.2025 godz. 12:15, sala 106 bud A-29
temat: Efficient neural network-enhanced multicopy estimation for quantum correlation measurements
prelegent: mgr Patrycja Tulewicz (Instytut Spintroniki i Informacji Kwantowej, UAM w Poznaniu)

Abstract:
Traditional approaches to quantifying quantum correlations, in particular entanglement and Bell nonlocality, typically rely on quantum state tomography (QST). Although versatile, QST suffers from the problem of exponential scaling, requiring resources that become prohibitive with system size. This limitation is a strong motivation to search for more efficient characterization methods that maintain measurement accuracy while significantly reduc-
ing resource requirements. In this talk, I will present an optimized multicopy estimation (MCE) technique that directly measures quantum correlation properties through specific singlet projection measurements performed on multiple copies of the quantum state. By integrating artificial neural networks into this approach, we have achieved a significant 67% reduction in measurement requirements compared to full QST. Our method maintains high accuracy while demonstrating excellent robustness to noise.
A key innovation in our approach is the use of SHAP (SHapley Additive exPlanations) analysis to identify the minimum set of informative measurements. Through this analysis, we have found that only five strategically chosen projection configurations are sufficient to reliably estimate both entanglement negativity and measures of Bell’s nonlocality. We experimentally verify our method on IBM quantum processors, demonstrating its practical advantages in real quantum hardware environments, where noise is unavoidable, and we have used maximum likelihood to mitigate it.
The neural network component of our system is trained to recognize patterns in the measurement data that correspond to specific quantum correlation properties, further enhancing the robustness of the method to experimental noise and device imperfections. This represents a significant advance over previous approaches that required more complex measurement protocols or were more susceptible to noise-induced errors.
We further demonstrate the utility of our method by applying it to benchmark distributed quantum computing (DQC) architectures, where it effectively captures how network topology and system parameters affect entanglement preservation. This application demonstrates the versatility of our approach for practical quantum system characterization where measurement resources are limited.