Presentation
Time-Aware Active Calibration and Scheduling for N-Qubit Systems via Bayesian Optimization and Reinforcement Learning
DescriptionFrequent recalibration of multi-qubit systems is essential to maintain hardware fidelity, but calibration itself is costly and subject to rapid parameter drift. This work presents a novel framework for time-aware active calibration that combines Bayesian Optimization (BO) and Reinforcement Learning (RL) to adaptively schedule and prioritize calibration experiments under uncertainty. We model the calibration landscape as a directed acyclic graph (DAG), where nodes represent hardware parameters (e.g., frequency, amplitude ratio, DRAG, or phase) and edges capture inter-parameter dependencies extracted automatically from Qiskit Experiments metadata. Each parameter's temporal drift is modeled as an Ornstein-Uhlenbeck (OU) or exponential decay process with uncertainty bounds. The BO agent selects the next calibration experiment to maximize expected information gain per unit time while respecting system-level constraints. A parallel RL/ILP scheduler coordinates cross-qubit calibration and readout tasks to minimize idle time and avoid conflicts.
Our system further incorporates self-triggered recalibration based on online RB/XEB health metrics, enabling autonomous recovery from drift without human intervention. Experimental evaluation on multi-qubit testbeds demonstrates improved calibration efficiency, longer mean time between recalibrations (MTBC), and reduced overall calibration overhead compared to fixed or purely heuristic scheduling baselines. We provide an open-source implementation, Cal Orchestrator, which integrates DAG construction, BO-based experiment selection, and RL-based calibration scheduling for scalable quantum system maintenance.
Our system further incorporates self-triggered recalibration based on online RB/XEB health metrics, enabling autonomous recovery from drift without human intervention. Experimental evaluation on multi-qubit testbeds demonstrates improved calibration efficiency, longer mean time between recalibrations (MTBC), and reduced overall calibration overhead compared to fixed or purely heuristic scheduling baselines. We provide an open-source implementation, Cal Orchestrator, which integrates DAG construction, BO-based experiment selection, and RL-based calibration scheduling for scalable quantum system maintenance.
Event Type
Research Manuscript
TimeTuesday, July 2812:03pm - 12:16pm PDT
LocationMtg Room 201B