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arXiv:2610.23316

SpinControl: Control Software for Prototyping and Benchmarking of Spin-Qubit Devices and Measurement Protocols

Harold B. Meerwaldt, Daniël P. van der Velde, Pieter T. Eendebak, Michiel J. Haye

quant-phcs.SESubmitted Oct 16, 2026• Announced Oct 19, 2026• 0 citations
arXivHTMLPDF

Abstract

Quantum computers require software to control the states of the qubits. As the actions on qubits grow in complexity with circuit execution, calibration, and benchmarking, the complexity of the control software grows in tandem. Spin-qubit quantum computers are at a stage where experimentalists need low-level access for tuning and calibration, and quantum algorithm researchers want high-level circuit-level access. In this article, we present the software design of SpinControl. SpinControl integrates three architectural metrics: 1) customizability, to allow prototyping devices and measurement protocols, is achieved by separating data and behaviour; 2) modularity and extensibility are achieved by separating functionalities into different components; and 3) robustness, to reliably execute quantum circuits on many qubits, is achieved by CI pipelines and input validation. SpinControl allows different types of measurements while customizing the instruments used, the pulse or circuit program executed, and the data processing. It allows building standardized experiments from measurement and analysis building blocks and executing these for calibration in a custom and conditional order. SpinControl implements these functionalities alongside robust configuration and data management, a connection to the cloud, and calibration scheduling. Pulse and circuit compilation, virtual gate matrices, as well as spin-qubit-specific GUIs integrated by SpinControl, make heavy use of software packages developed by Delft University of Technology: pulse lib, core-tools (both open source), and QConstruct

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11 pages, 8 figures

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Being important parts of the superconducting quantum computer, the high-speed arbitrary waveform generator (AWG), ultraprecision dc source, and high-speed digitizer are used to manipulate the qubit. The complexity of an experimental setup increases rapidly as the number of qubits grows. Cumbersome instrument management, distortion of signals, and inefficiency of data transmission are gradually highlighted and become the bottlenecks in scaling up the number of qubits. In addition, fault-tolerant quantum computing has real-time feedback requirements of qubit states. To deal with these challenges, we propose an instrument management software design in this paper. The software maps the resources of separate instruments to a unified virtual instrument, achieving the scalability of the instruments. The processing and correction of signals are deployed on a server, which automatically corrects the distortion of the signals. By designing a multi-threaded mechanism for the AWG and a custom-defined data-link protocol for the digitizer, the efficiency of the software meets the requirements of experiments. Cooperating with the AWG and digitizer, the software provides the real-time feedback capability using an instruction compiler. Thus, the software meets the requirements of superconducting quantum computing, as demonstrated through its application to a 12-bit quantum experiment.
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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.