X38.00009. Resonant Coupling Parameter Estimation with Superconducting Qubits

Presented by: Jérémy Béjanin


Today's quantum computers comprise tens of qubits, which are interconnected either directly or indirectly via various types of coupling elements. In order to calibrate and operate such systems it is necessary to have a complete knowledge of all parameters in the Hamiltonian describing the system. In this article, we demonstrate a method to efficiently learn the parameters of resonant interactions for quantum computers consisting of frequency-tunable superconducting qubits. Such interactions include, for example, those to other qubits, resonators, two-level state defects, or other unwanted modes. Our method is based on a significantly improved swap spectroscopy calibration and consists of an offline data collection algorithm, followed by an online Bayesian learning algorithm. The purpose of the offline algorithm is to detect and roughly estimate resonant interactions from a state of zero knowledge. It produces a square root reduction in the number of measurements. The online algorithm subsequently refines the estimate of the parameters to comparable accuracy as the standard method, but in constant time. The experimental implementation of our technique shortens the qubit calibration time by an order of magnitude. We believe the method investigated will improve present medium-scale superconducting quantum computers and will also scale up to larger systems. Finally, the two algorithms presented can readily be adopted by communities working on different physical implementations of quantum computing architectures.


  • Jérémy Béjanin
  • Carolyn Earnest
  • Matteo Mariantoni
  • Yuval R Sanders


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