New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for calibration or decoherence mitigation on neighboring data qubits, but little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization. Techniques from classical estimation and dynamic control, suitably adapted to the strictures of quantum measurement, provide an opportunity to extract augmented hardware performance through automation of low-level characterization and control. In this work, we present an adaptive learning framework, Noise Mapping for Quantum Architectures (NMQA), for scheduling of sensor–qubit measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Via a two-layer particle filter, NMQA 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 NMQA outperforms brute-force mapping by up to 20× (3×) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.

新的量子计算体系结构考虑将量子位集成为传感器，以提供可操作的信息，这些信息可用于对相邻数据量子位进行校准或消除相干衰减，但是很少有工作解决如何有效实施此类方案以最大化信息利用率的问题。来自经典估计和动态控制的技术，特别适合于量子测量的局限性，提供了通过低级表征和控制自动化来提取增强的硬件性能的机会。在这项工作中，我们提出了一种自适应学习框架，即“量子体系结构的噪声映射（NMQA）”，用于安排跨设备体系结构的传感器-量子位测量和有效的空间噪声映射（在激活之前）。通过两层颗粒过滤器 NMQA接收二进制测量值并确定体系结构中共享常见噪声过程的区域；然后，自适应控制器调度将来的测量以减少地图的不确定性。在一系列捕获的ions离子上进行的数值分析和实验表明，在模拟（实验）中，NMQA的强力映射性能高达20倍（3倍），这是通过映射空间不均匀磁性所需的测量次数减少而得出的。具有目标错误指标的字段。作为机器人控制对量子设备的早期适应，这项工作为量子计算机科学开辟了令人兴奋的新途径。在一系列捕获的ions离子上进行的数值分析和实验表明，在模拟（实验）中，NMQA的强力映射性能高达20倍（3倍），这是通过映射空间不均匀磁性所需的测量次数减少而得出的。具有目标错误指标的字段。作为机器人控制对量子设备的早期适应，这项工作为量子计算机科学开辟了令人兴奋的新途径。在一系列捕获的ions离子上进行的数值分析和实验表明，在模拟（实验）中，NMQA的强力映射性能高达20倍（3倍），这是通过映射空间不均匀磁性所需的测量次数减少而得出的。具有目标错误指标的字段。作为机器人控制对量子设备的早期适应，这项工作为量子计算机科学开辟了令人兴奋的新途径。