This article discusses a method developed to robustly learn the Hamiltonian dynamics of superconducting quantum processors, which are essential for advancing quantum simulations capable of outperforming classical computing. Researchers created a scalable Hamiltonian learning algorithm that effectively estimates free Hamiltonian parameters from time-series data of up to 14 qubits, while accounting for errors in state preparation and measurement (SPAM). Key components of their approach include a novel super-resolution technique for frequency extraction, tensorESPRIT, and constrained manifold optimization, which improve the identification of Hamiltonian parameters to sub-MHz accuracy. This study not only enhances the understanding and calibration of analog quantum simulators but also provides diagnostic tools for improving their performance in simulating complex quantum systems. The findings signify a step forward in the quest for precise characterization of quantum processors, laying the groundwork for future developments in quantum computing and simulation.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41467-024-52629-3
Please consult also the Quantum Server Marketplace platform for the outsourcing of computational science R&D projects to external expert consultants through remote collaborations:
#materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation