2020/02/04 by Mauro Valeri, Valeri, Mauro, Emanuele Polino +15 · 4 citations
Computer Science · #FOS: Physical sciences #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2002.01232
openalex publication_date 2020/02/04 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Achieving ultimate bounds in estimation processes is the main objective of\nquantum metrology. In this context, several problems require measurement of\nmultiple parameters by employing only a limited amount of resources. To this\nend, adaptive protocols, exploiting additional control parameters, provide a\ntool to optimize the performance of a quantum sensor to work in such limited\ndata regime. Finding the optimal strategies to tune the control parameters\nduring the estimation process is a non-trivial problem, and machine learning\ntechniques are a natural solution to address such task. Here, we investigate\nand implement experimentally for the first time an adaptive Bayesian\nmultiparameter estimation technique tailored to reach optimal performances with\nvery limited data. We employ a compact and flexible integrated photonic\ncircuit, fabricated by femtosecond laser writing, which allows to implement\ndifferent strategies with high degree of control. The obtained results show\nthat adaptive strategies can become a viable approach for realistic sensors\nworking with a limited amount of resources.\n