We introduce two classes of lightweight, adaptive calibration protocols for quantum computers that leverage fast feedback. The first enables shot-by-shot updates to device parameters using measurement outcomes from simple, indefinite-outcome quantum circuits. This low-latency approach supports rapid tuning of one or more parameters in real time to mitigate drift. The second protocol updates parameters after collecting measurements from definite-outcome circuits (e.g.~syndrome extraction circuits for quantum error correction), balancing efficiency with classical control overheads. We use numerical simulations to demonstrate that both methods can calibrate 1- and 2-qubit gates rapidly and accurately even in the presence of decoherence, state preparation and measurement (SPAM) errors, and parameter drift. We propose and demonstrate effective adaptive strategies for tuning the hyperparameters of both protocols. Finally, we demonstrate the feasibility of real-time in-situ calibration of qubits performing quantum error correction, using only syndrome data, via numerical simulations of syndrome extraction in the [[5,1,3]] code.
In superconducting devices running variational workloads, gate and readout fidelities drift on hour timescales, while existing runtime schedulers treat backend quality as static. The temporal dimension of calibration remains unresolved. We formulate runtime calibration as a state-trajectory feedback-control problem under a fixed wall-clock budget, and investigate whether spending time on calibration now can improve the future optimization trajectory. Calibration quality proxy is represented as a drifting equivalent-age state, recovery action is modeled as costly state reset, and policies are evaluated by time-integrated optimization gap over the full execution window. Using a finite-horizon rollout controller, we compare feedback calibration against a strengthened family of open-loop baselines across three latency regimes: cloud-like (25 ms), local-millisecond (1 ms), and tight-loop (4
μs). The results show a clear ordering: cloud-like feedback is generally uncompetitive, while local-ms and tight-loop regimes open a positive-gain region that grows with workload quality-sensitivity and initial calibration age. Crucially, the gap between local-ms and tight-loop control is modest for single-target recovery. The advantage of tight-loop integration emerges under capacity pressure, when many calibration targets must be processed within the same control window.
In order to develop quantum computing systems towards practically useful applications, their physical quantum bits (qubits) must be able to operate with minimal error. Recent work has demonstrated stochastic gate calibration protocols for quantum systems which are meant to track drifting control parameters and tune gate operations to high fidelity. These protocols critically rely on low-latency feedback between the quantum system and its classical control hardware, which is impossible without on-board classical compute from FPGAs or ASICs. In this work, we analyze the performance of a single-shot stochastic calibration protocol for indefinite outcome quantum circuits under various latency conditions based on timing considerations from experimental quantum systems. We also demonstrate the benefits that can be achieved with ASIC implementation of the protocol by synthesizing the classical control logic in a 28 nm CMOS design node, with simulations extended to 14 nm FinFET and at both room and cryogenic temperatures. We show that these classes of quantum calibration protocols can be easily implemented within contemporary control system architectures for low-latency performance without significant power or resource utilization, allowing for the rapid tuning and drift control of any gate-model quantum system towards fault-tolerant computation.