Resonant Coupling Parameter Estimation with Superconducting Qubits

Abstract

Today's quantum computers are comprised of tens of qubits interacting with each other and the environment in increasingly complex networks. In order to achieve the best possible performance when operating such systems, it is necessary to have accurate knowledge of all parameters in the quantum computer Hamiltonian. In this article, we demonstrate theoretically and experimentally 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 traditional swap spectroscopy calibration, but in constant time. We perform an experiment implementing our technique with a superconducting qubit. By combining both algorithms, we observe a reduction of the calibration time by one 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 here can be readily adopted by communities working on different physical implementations of quantum computing architectures.

Interacting Defects Generate Stochastic Fluctuations in Superconducting Qubits

“…We pick f Q-TLS uniformly at random from a frequency range relevant to our experiments. Since f q ∼ 4.5 GHz, we generate Q-TLSs with f Q-TLS ∈ [4,5] GHz.…”

“…In the case of Q-TLSs, their number N Q-TLS can be readily obtained by counting the interactions between a qubit and a Q-TLS in spectroscopy experiments [5,7,17,43]. For qubits where Q-TLSs are hosted in a volume of native oxide, the estimated density is D Q-TLS ∼ 100 GHz −1 µm −3 .…”

“…The superconducting Xmon transmon qubit [7] used in this article is the same as in our work of Ref. [5], with micrographs shown in that manuscript. The qubit consists of an Al island in parallel with a superconducting quantum interference device (SQUID).…”

“…The setup is the same as in our work of Ref. [5], which shows a detailed diagram of the control and measurement lines.…”

“…A few examples of planar devices can be found in our works of Refs. [4] and [5], where we have investigated coplanar waveguide (CPW) resonators [6] as well as Xmon transmon qubits [7].…”