Adaptive scheduling of noise characterization in quantum computers

New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for decoherence mitigation on neighboring data qubits. Little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization when noise fields possess long-range correlations. We present an autonomous learning framework, Quantum Simultaneous Localization and Mapping (QSLAM), for adaptive scheduling of sensor-qubit measurements and efficient (in measurement number and time) spatial noise mapping across device architectures. Via a two-layer particle filter, QSLAM receives binary measurements and determines regions within the architecture that share common noise processes; an adaptive controller then schedules future measurements to reduce map uncertainty. Numerical analysis and experiments on an array of trapped ytterbium ions demonstrate that QSLAM outperforms brute-force mapping by up-to $18$x ($3$x) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target fidelity. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.