Agradecimentos: The authors are grateful for financial support from the Brazilian Agencies FAPESP, CNPq, and CAPES. L.K.C., R.d.J.N., and F.F.F. thank the Brazilian Agency FAPESP (Grants No. 2019/09624-3, No. 2018/00796-3, No. 2021/04655-8, and No. 2023/04987-6) and also National Institute of Science and Technology for Quantum Information (CNPq INCT-IQ, Grant No. 465469/2014-0) for supporting this research. F.S.L. thanks the UNICAMP Postdoctoral Researcher Program for financial support. F.F.F. also acknowledges support from ONR, Project No. N62909-24-1-2012 Ver menos

Abstract: The precision required to characterize a Hamiltonian is central to developing advantageous quantum computers, providing powerful advances in quantum sensing and crosstalk mitigation. Traditional methods to determine a Hamiltonian are difficult due to the intricacies of quantum systems, involving numbers of equations and parameters that grow exponentially with the number of qubits. To mitigate these shortcomings, in this paper, we introduce an innovative and effective procedure integrating a physics-informed neural network (PINN) with a freezing mechanism to learn the Hamiltonian parameters efficiently. Although PINN and experimental data alone would become impractical as N increases, the mechanism we introduce freezes the interactions of most of the qubits, leaving just a qubit subsystem to be analyzed by the PINN method. Determination of all physical parameters is accomplished by analyzing the system by parts until completion. We validated the efficacy of our method using simulation data obtained from the IBM quantum computer to obtain the training data and we found that a PINN can learn the two-qubit parameters with high accuracy, achieving a median error of less than 0.1% for systems of up to four qubits. We have successfully combined the PINN analysis of two qubits with the freezing mechanism in the case of a four-qubit system Ver menos