Alice Pagano
Alice Pagano is a physicist working with the Google Quantum AI team, having joined the effort in 2025. Driven by a passion for deep technological challenges, their work focuses on advancing the boundaries of quantum computing. Originally from Italy, Alice Pagano earned their Bachelor's and Master's degrees in Physics from the University of Padova. They subsequently completed their Ph.D. at the University of Ulm in Germany.
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Obtaining accurate representations of the eigenstates of an array of coupled superconducting qubits is a crucial step in the design of circuit quantum electrodynamics (circuit-QED)-based quantum processors. However, exact diagonalization of the device Hamiltonian is challenging for system sizes beyond tens of qubits. Here, we employ a tensor network method based on the density matrix renormalization group (DMRG) algorithm, DMRG-X, to efficiently obtain localized eigenstates of a 2D transmon array without the need to first compute lower-energy states. We also introduce MTDMRG-X, a new algorithm that combines DMRG-X with multi-target DMRG to efficiently compute excited states even in regimes with strong eigenstate hybridization. We showcase the use of these methods for the analysis of long-range couplings in a multi-transmon Hamiltonian including qubits and couplers, and we discuss eigenstate localization. These developments facilitate the design and parameter optimization of large-scale superconducting quantum processors.
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