Autonomous adaptive noise characterization in quantum computers

TL;DR

This paper introduces NMQA, an autonomous framework that adaptively schedules measurements to efficiently map spatial noise in quantum computers, significantly reducing measurement requirements and enhancing noise characterization.

Contribution

The work presents a novel adaptive learning framework using a two-layer particle filter for efficient noise mapping in quantum architectures, integrating classical estimation with quantum measurement constraints.

Findings

NMQA reduces measurement count by up to 18 times in simulations.

NMQA achieves up to 3 times reduction in experiments.

Effective spatial noise mapping in quantum devices demonstrated.

Abstract

New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for 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 autonomous learning framework, Noise Mapping for Quantum Architectures (NMQA), for adaptive 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 $18$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 error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.