Accelerating Quantum Technology Development with Machine Learning

Project Information

QruiseOS

Grant agreement ID: 101099538

DOI

10.3030/101099538

Project closed

EC signature date 11 November 2022

Start date 1 January 2023

End date 30 September 2025

Funded under

• The European Innovation Council (EIC)

Total cost

€ 2 499 000,00

EU contribution

€ 2 499 000,00

Coordinated by

QRUISE GMBH
Germany

Periodic Reporting for period 1 - QruiseOS (Accelerating Quantum Technology Development with MachineLearning)

Reporting period: 2023-01-01 to 2023-12-31

Summary of the context and overall objectives of the project

The field of quantum technologies encompasses a wide range of very different devices which must operate at the very edge of what is feasible, results are noisy, and only become clear if repeated many times and careful statistics are gathered. We are applying optimal control theory, optimization theory and machine learning to the challenge of controlling, characterizing and calibrating quantum technology devices in general, and quantum computers specifically. Qruise (as in cruise control) GmbH is a young software startup from Forschungszentrum Jülich and Padova University, and the OpenSuperQ EU quantum flagship project, created for the purpose of commercializing the founders’ collective experience in control of quantum systems. Our project will increase the maturity of the Qruise technology via TRL 5-6 level activities of research, technology development and validation with pilot partners. In the end, we will deliver a viable demonstrator with fully vertically integrated components: QruiseOS.
With QruiseOS, quantum technology development can be significantly accelerated, allowing even small academic labs and startups to compete with the large players in the field. This project covers all necessary steps for full commercial readiness.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Work performed and main achievements across the tasks include the development and validation of components for optimal control and characterization of quantum devices by Qruise, culminating in a single integrated workflow for device characterization. Innovative calibration strategies, such as smart predictive calibration and parallelized bring-up for multi-qubit chips, were designed to enhance operational efficiency. The exploration of Bayesian Adaptive Experiment Design through deep learning has shown potential for improving experiment setup efficiency, despite challenges in implementation. Efforts in reinforcement-learning based optimization have led to promising approaches for generating optimal pulse shapes, with ongoing integration into QruiseOS for real hardware testing. Additionally, the expansion of the Qruise library to include advanced visualization tools for quantum dynamics showcases significant progress in making quantum computing more accessible and comprehensible.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

A cloud-based graph database was implemented to seamlessly manage quantum computing stack models, offering version control and extensive permission mechanisms for improved device control and simulation fidelity. The creation of an Experiment Data Lake enables the efficient storage and utilization of experiment results, enhancing the capability for machine learning-driven device characterization and monitoring. Additionally, the development of QruiseOS represents a significant step towards a full-stack quantum operating system, integrating calibration, characterization, and high-level programming tools into a single, unified platform, currently compatible with different control electronics, with plans for broader support.