Parasitic two-level-system (TLS) defects are a major source of energy relaxation and temporal instability in superconducting quantum processors. Our sub-second adaptive spectroscopyreveals telegraphic switching of TLSs with a characteristic timescale of a few seconds and spectral diffusion with diffusivity D≈0.9 MHz2/s. These timescales are about 3×102 times faster than what is observed in conventional nonadaptive spectroscopy, which typically requires hours of measurement time. We resolve such fast dynamics on a field-programmable gate array (FPGA)-based controller that enables measurement of frequency- and time-resolved relaxations with sub-second temporal resolution in flux-tunable superconducting qubits. We observe similar defect dynamics across multiple qubits in independently fabricated devices measured in different laboratories. We correlate TLS-induced fluctuations with gate-level errors using randomized benchmarking. Our results reveal a previously inaccessible regime of frequency-resolved TLS dynamics and redefine the timescales relevant to TLS-aware characterization and calibration of superconducting quantum processors.
The fidelity of operations on a solid-state quantum processor is ultimately bounded by decoherence effects induced by a fluctuating environment. Characterizing environmental fluctuationsis challenging because the acquisition time of experimental protocols limits the precision with which the environment can be measured and may obscure the detailed structure of these fluctuations. Here we present a real-time Bayesian method for estimating the relaxation rate of a qubit, leveraging a classical controller with an integrated field-programmable gate array (FPGA). Using our FPGA-powered Bayesian method, we adaptively and continuously track the relaxation-time fluctuations of two fixed-frequency superconducting transmon qubits, which exhibit average relaxation times of approximately 0.17 ms and occasionally exceed 0.5 ms. Our technique allows for the estimation of these relaxation times in a few milliseconds, more than two orders of magnitude faster than previous nonadaptive methods, and allows us to observe fluctuations up to 5 times the qubit’s average relaxation rates on significantly shorter timescales than previously reported. Our statistical analysis reveals that these fluctuations occur on much faster timescales than previously understood, with two-level-system switching rates reaching up to 10 Hz. Our work offers an appealing solution for rapid relaxation-rate characterization in device screening and for improved understanding of fast relaxation dynamics.
We present a real-time method for calibrating the frequency of a resonantly driven qubit. The real-time processing capabilities of a controller dynamically compute adaptive probingsequences for qubit-frequency estimation. Each probing time and drive frequency are calculated to divide the prior probability distribution into two branches, following a locally optimal strategy that mimics a conventional binary search. We show the algorithm’s efficacy by stabilizing a flux-tunable transmon qubit, leading to improved coherence and gate fidelity. By feeding forward the updated qubit frequency, the FPGA-powered control electronics also mitigates non-Markovian noise in the system, which is detrimental to quantum error correction. Our protocol highlights the importance of feedback in improving the calibration and stability of qubits subject to drift and can be readily applied to other qubit platforms.