Autonomous Bootstrapping of Quantum Dot Devices (2407.20061v2)

Abstract: Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can fully autonomously handle the increasing number of control parameters are becoming essential for enabling scalability. We propose a bootstrapping algorithm for initializing a depletion-mode QD device in preparation for subsequent phases of tuning. During bootstrapping, the QD device functionality is validated, all gates are characterized, and the QD charge sensor is made operational. We demonstrate the bootstrapping protocol in conjunction with a coarse-tuning module, showing that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global-state configuration in under 8 min with a success rate of 96 %. Finally, by following heuristic approaches to QD device initialization and combining the efficient ray-based measurement with the rapid radio-frequency reflectometry measurements, the proposed algorithm establishes a reference in terms of performance, reliability, and efficiency against which alternative algorithms can be benchmarked.

• The paper introduces a bootstrapping process that autonomously initializes quantum dot devices with a 96% success rate in roughly 8 minutes.
• The methodology combines heuristic techniques with ray-based measurements and rf reflectometry to efficiently characterize gate operations and activate charge sensors.
• The automation framework reduces manual tuning, paving the way for scalable, high-fidelity quantum dot arrays and enhanced integration with machine learning.

Autonomous Bootstrapping of Quantum Dot Devices: A Methodical Advancement in Automated Qubit Initialization

The advancing complexity of quantum dot (QD) arrays necessitates robust and autonomous tuning procedures to facilitate scalable quantum computing systems. Quantum dot arrays, particularly gate-defined ones in semiconductor materials such as GaAs and Si/SiGe, present a promising platform for realizing high-fidelity and scalable qubit implementations. However, their applicability hinges significantly on the ability to efficiently tune these multidimensional systems with minimal human intervention.

In "Autonomous Bootstrapping of Quantum Dot Devices," the authors propose an innovative bootstrapping algorithm designed to automate the daunting task of initializing QD devices from a pristine state to an operational regime ready for further tuning. This approach is essential for the scaling of QD arrays, addressing the complex task of managing the increasing number of control parameters necessary for effective QD operation.

Key Contributions and Methodology

The core contribution of this paper lies in the introduction of a bootstrapping process that incorporates both heuristic approaches and ray-based measurement techniques. This process aids in establishing an operational regime by characterizing device gates, validating functionality, and preparing charge sensors necessary for the next stages of qubit calibration. Specifically, it leverages a proximal charge sensor and rapid radio-frequency (rf) reflectometry for more efficient and reliable device tuning compared to traditional transport measurements.

The experiment utilizes a gate-defined, depletion-mode GaAs/Al$_{0.36}$Ga$_{0.64}$As device that is designed with tunable metallic electrodes sharing components with industrial designs. Upon cooling the device to millikelvin temperatures, the bootstrapping algorithm efficiently initializes the device, ensuring the charge sensing systems are operational and providing well-characterized gate operation ranges.

An important aspect of this paper is its emphasis on the diagnostic and characterization phase of the bootstrapping process. The method employs sophisticated techniques, including ray-based measurements, to ensure comprehensive characterization of the voltage space and rigorously verify the viability of the sensor QD. This step is pivotal for achieving high performance and reliability necessary for scaling to larger quantum processor implementations.

Numerical Results and Efficiency

Remarkably, the proposed bootstrapping algorithm combined with a coarse tuning module achieved an impressive success rate of 96% in bringing a cooled-down QD device to the desired global state configuration in approximately 8 minutes. This efficient approach is characterized by a completion rate of 86.5% in bootstrapping tests, with coarse tuning achieving 96.6% success in further calibrating the quantum dots to a state suitable for quantum computation.

Impact and Future Developments

The implications of this research are notable both in theoretical and practical domains. Practically, this advancement in automated tuning lays the groundwork for the development of fully integrated systems capable of supporting large 1D and 2D arrays of quantum dots. Such systems can benefit from reduced manual intervention, leading to decreased costs and time associated with human-led device calibration and enabling researchers to focus on higher-level quantum operations.

Theoretically, this method demonstrates the successful cross-platform application of machine learning models trained on synthetic data, which can be expanded to various device architectures. This cross-platform adaptability suggests that the application of machine learning in physical experiments can streamline processes significantly, enhancing the reproducibility and reliability of quantum computing devices.

Going forward, the authors suggest enhancements in data quality control to bolster the reliability of machine learning predictions in the tuning system. They also aim to extend this bootstrapping algorithm to multi-QD arrays, further expanding the scope of its applicability within the field. Such developments will undoubtedly contribute to more efficient quantum computing systems by refining the processes of qubit initialization and strengthening the integration of quantum dot devices with automated controls.

Thus, this paper not only proposes a methodologically sound innovation in the field of quantum dot device tuning but also sets a substantial precedent for leveraging automation in future quantum computing frameworks.