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In a recent publication appeard on npj Quantum Information scientists from Sapienza University of Rome in a collaboration with Consiglio Nazionale delle Ricerche - Istituto di Fotonica e Nanotecnologie (CNR-IFN), developed a variational approach for the optimization of a quantum photonic sensor.
Variational quantum metrology represents a powerful tool for optimizing estimation strategies, resulting particularly beneficial for multiparameter estimation problems, that often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, in this work it is demonstrated how a variational approach allows to efficiently optimize a quantum multiphase sensor. Leveraging the reconfigurability of an integrated photonic device, a hybrid quantum-classical feedback loop is implemented to enhance estimation performance. Quantum circuit evaluations are utilized to compute the system's partial derivatives using the parameter-shift rule, thus experimentally reconstructing the Fisher information matrix. This, in turn, serves as the cost function for a classical learning algorithm aimed at optimizing measurement settings. Experimental results showcase significant improvements in estimation accuracy and noise robustness, underscoring the potential of variational techniques for practical applications in quantum sensing and more broadly in quantum information processing using photonic circuits.
The full article by V. Cimini, M. Valeri, S. Piacentini, F. Ceccarelli, G. Corrielli, R. Osellame, N. Spagnolo, and F. Sciarrino, “Variational quantum algorithm for experimental photonic multiparameter estimation”, npj Quantum Information 10, 26 (2024) is available here