Performing efficient quantum computer tune-up and calibration is essential for growth in system complexity. In this work we explore the link between facilitating such capabilities and the underlying architecture of the physical hardware. We focus on the specific challenge of measuring (``mapping'') spatially inhomogeneous quasistatic calibration errors using spectator qubits dedicated to the task of sensing and calibration. We introduce an architectural concept for such spectator qubits: arranging them spatially according to prescriptions from optimal two-dimensional approximation theory. We show that this insight allows for efficient reconstruction of inhomogeneities in qubit calibration, focusing on the specific example of frequency errors which may arise from fabrication variances or ambient magnetic fields. Our results demonstrate that optimal interpolation techniques display near optimal error scaling in cases where the measured characteristic (here the qubit frequency) varies smoothly, and we probe the limits of these benefits as a function of measurement uncertainty. For more complex spatial variations, we demonstrate that the noise mapping for quantum architectures formalism for adaptive measurement and noise filtering outperforms optimal interpolation techniques in isolation and, crucially, can be combined with insights from optimal interpolation theory to produce a general purpose protocol.
Performing efficient quantum computer tune-up and calibration is essential for growth in system complexity. In this work we explore the link between facilitating such capabilities and the underlying architecture of the physical hardware. We focus on the specific challenge of measuring (``mapping'') spatially inhomogeneous quasistatic calibration errors using spectator qubits dedicated to the task of sensing and calibration. We introduce an architectural concept for such spectator qubits: arranging them spatially according to prescriptions from optimal two-dimensional approximation theory. We show that this insight allows for efficient reconstruction of inhomogeneities in qubit calibration, focusing on the specific example of frequency errors which may arise from fabrication variances or ambient magnetic fields. Our results demonstrate that optimal interpolation techniques display near optimal error scaling in cases where the measured characteristic (here the qubit frequency) varies smoothly, and we probe the limits of these benefits as a function of measurement uncertainty. For more complex spatial variations, we demonstrate that the noise mapping for quantum architectures formalism for adaptive measurement and noise filtering outperforms optimal interpolation techniques in isolation and, crucially, can be combined with insights from optimal interpolation theory to produce a general purpose protocol.
Abstract 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.
We present a formal analysis and convergence proofs for an autonomous adaptive learning algorithm useful for the tuneup and stabilization of quantum computing architectures. We focus on the specific application of spatial noise mapping in a spectator qubit paradigm, in which these qubits act as sensors to provide information useful for decoherence mitigation on proximal data qubits. In earlier work, the authors introduced and experimentally demonstrated this framework, Noise Mapping for Quantum Architectures (NMQA), as applied in autonomous adaptive scheduling of sensor-qubit measurements across multi-qubit architectures. This methodology has several unique features: a classical measurement model that incorporates discretized single-qubit projective measurements, and a two-layered particle filtering structure to facilitate adaptive information sharing between qubits in small spatial regions. In this work, we formalize the NMQA problem definition and build direct links to conventional non-linear filtering theory. Taking into account NMQA's departure from existing literature, we show that NMQA satisfies axioms of theorems for asymptotic convergence in specific parameter regimes. Outside of these parameter regimes, we augment our theoretical analysis with numerical studies to estimate rates of convergence for NMQA. We find that these numerical estimates match our theoretical expectations for a variety of physical configurations. Our work ensures that the methodology encapsulated by NMQA permits comparative analysis with existing filtering techniques in the literature, while highlighting unique directions for future automation of quantum computer tuneup, calibration, and stabilization.
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 18x (3x) 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.
Superconducting qubits are a leading platform for quantum computation, yet decoherence and scalability challenges continue to limit progress toward fault-tolerant systems. This review synthesizes research on architectures, noise sources, and error correction to clarify performance trade-offs and guide development. The transition from Transmon to Fluxonium has dramatically improved qubit performance, achieving millisecond-scale coherence times. However, this success reveals a fundamental limit of coherence now being primarily restricted by the physical properties of the materials, specifically dielectric losses occurring at the Josephson junction leads and within the surrounding materials. As overcoming these material limits becomes increasingly difficult, system-level engineering determines whether high-coherence qubits can scale.Scaling quantum systems is difficult because current cooling and control systems create too much heat and this forces a shift to integrated digital superconducting circuits and cryogenic light-based links. While these hardware challenges restrict scaling, experiments in error correction show that logical qubits can already outperform physical ones. For example, a distance-7 surface code improved logical qubit lifetime by 2.4×. Achieving practical fault tolerance, however, requires moving beyond simple error models. Future systems must combine techniques like Dynamical Decoupling and Machine Learning-based Optimal Control with dynamic classical control capable of handling complex, correlated faults such as Multi-Bit Burst Errors. Progress in materials, cryogenic system integration for control, and adaptive error management must advance together to realize scalable, high-fidelity quantum hardware.
Decoherence remains a major challenge in Near term, Intermediate scale Noisy Quantum (NISQ) computers. This thesis proposes techniques for characterizing classical noise correlations and performance variations in NISQ devices using single-qubit projective measurements. The central tasks of qubit state estimation and prediction are considered for measurements that are either temporally or spatially correlated. Firstly, this thesis focuses on timeseries prediction for single-qubit measurement outcomes. Focusing on repeated Ramsey measurements performed on a single qubit subject to temporally correlated dephasing, the key challenge is to predict qubit state dynamics by learning temporal noise correlations in a real-time stream of incoming measurements. Autoregressive or Fourier-based Kalman Filtering (KF) protocols are investigated for maximizing the forward prediction horizon and autoregressive approaches demonstrate superior predictive capabilities. Secondly, this thesis investigates the utility of nonlinear stochastic methods to characterize quantum systems using spatially correlated single-qubit projective measurements, via particle filters. A novel likelihood function is presented and incorporated into conventional particle filters. An adaptive procedure, `NMQA', is proposed to characterize a spatially inhomogeneous dephasing field in 2D by allocating measurements on a multi-qubit array. NMQA outperforms brute-force mapping in simulations and using experimental data. Finally, spatial prediction in 2D is compared with bivariate interpolation on a geometric arrangement of points known as the Padua points. By measuring a spatially inhomogeneous dephasing field on dedicated `sensor qubits', the objective is to predict the value of the field on unmeasured, proximal `data qubits'. The number and arrangement of sensor qubits relative to a fixed lattice of data qubits is investigated, providing insights for a general purpose predictive mapping protocol.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.