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arXiv:2608.10325

Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot

Johnathan Bryan, Tim J. Wilson, H. W. Jiang

Published Aug 10, 2026• 0 citations
arXivHTMLPDF

Abstract

Long term operation of semiconductor spin qubits requires active stabilization of quantum dot potentials against low-frequency charge noise. To address this, we demonstrate a gradient-descent feedback approach in a silicon double quantum dot device using high-bandwidth radio-frequency (rf) reflectometry readout and a field-programmable gate array (FPGA) for digital signal processing. This architecture enables continuous multi-parameter gradient calculation and quick gate voltage updates, significantly increasing the effective feedback bandwidth compared to previous methods relying on slow direct-current transport measurements. By operating with 8 steps per feedback cycle and an integration time of 25.6 $μ$s, this high-speed stabilization scheme maintains better than -6 dB of noise suppression up to 5 kHz, achieving an attenuation of $-7.4 \pm 0.2$ dB at the maximum measured frequency. This effectively suppresses low-frequency 1/f noise, maintaining device stability over longer time periods, and potentially enables real-time control in large-scale quantum dot arrays.

DOI
10.48550/arxiv.2608.10325

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Long term operation of semiconductor spin qubits requires active stabilization of quantum dot potentials against low-frequency charge noise. To address this, we demonstrate a gradient-descent feedback approach in a silicon double quantum dot device using high-bandwidth radio-frequency (rf) reflectometry readout and a field-programmable gate array (FPGA) for digital signal processing. This architecture enables continuous multi-parameter gradient calculation and quick gate voltage updates, significantly increasing the effective feedback bandwidth compared to previous methods relying on slow direct-current transport measurements. By operating with 8 steps per feedback cycle and an integration time of 25.6 μμμs, this high-speed stabilization scheme maintains better than -6 dB of noise suppression up to 5 kHz, achieving an attenuation of −7.4±0.2-7.4 \pm 0.2−7.4±0.2 dB at the maximum measured frequency. This effectively suppresses low-frequency 1/f noise, maintaining device stability over longer time periods, and potentially enables real-time control in large-scale quantum dot arrays.
Abstract With a view to long-term qubit device operation, we report on a method to stabilize a silicon double quantum dot against slow drift in a two-dimensional gate-voltage space based on a current gradient-based feedback technique. We demonstrate that, unlike conventional single-axis feedback schemes, our method can maintain the double-dot potential configuration. We measure a feedback bandwidth of up to 300 mHz, consistent with the sampling rate and the digital filter cutoff frequency used in the experiment.
The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a need for autonomous stabilization frameworks. Here, we introduce a method that uses the full network of charge-transition lines in repeatedly acquired double-quantum-dot charge stability diagrams (CSDs) as a multidimensional probe of the local electrostatic environment. By accurately tracking the motion of selected transitions in time, we detect voltage drifts, identify abrupt charge reconfigurations, and apply compensating updates to maintain stable operating conditions. We demonstrate our approach on a 10-QD device, showing robust stabilization and real-time diagnostic access to dot-specific noise processes. The high acquisition rate of radio-frequency reflectometry CSD measurements also enables time-domain noise spectroscopy, allowing the extraction of noise power spectral densities, the identification of two-level fluctuators, and the analysis of spatial noise correlations across the array. From our analysis, we find that the background noise at 100~μμμ\si{\hertz} is dominated by drift with a power law of 1/f21/f^21/f2, accompanied by a few dominant two-level fluctuators and an average linear correlation length of (188±38)(188 \pm 38)(188±38)~\si{\nano\meter} in the device. These capabilities form the basis of a scalable, autonomous calibration and characterization module for QD-based quantum processors, providing essential feedback for long-duration, high-fidelity qubit operations.
Spins in silicon quantum devices are promising candidates for large-scale quantum computing. Gate-based sensing of spin qubits offers a compact and scalable readout with high fidelity, however, further improvements in sensitivity are required to meet the fidelity thresholds and measurement timescales needed for the implementation of fast feedback in error correction protocols. Here, we combine radio-frequency gate-based sensing at 622 MHz with a Josephson parametric amplifier, that operates in the 500-800 MHz band, to reduce the integration time required to read the state of a silicon double quantum dot formed in a nanowire transistor. Based on our achieved signal-to-noise ratio, we estimate that singlet-triplet single-shot readout with an average fidelity of 99.7% could be performed in 1 μs, well below the requirements for fault-tolerant readout and 30 times faster than without the Josephson parametric amplifier. Additionally, the Josephson parametric amplifier allows operation at a lower radio-frequency power while maintaining identical signal-to-noise ratio. We determine a noise temperature of 200 mK with a contribution from the Josephson parametric amplifier (25%), cryogenic amplifier (25%) and the resonator (50%), showing routes to further increase the readout speed.
Solid-state quantum technologies such as quantum dot qubits and quantum electrical metrology circuits rely on quantum phenomena at ultra-low energies, making them highly sensitive to various forms of environmental noise. Conventional passive filtering schemes can reduce high-frequency noise but are often ineffective against low-frequency interference, like powerline or instrument-induced. Extending such filters to lower frequencies causes practical issues such as longer stabilization times, slower system response, and increased Johnson noise, which impede low-frequency transport measurements. To address these limitations, we propose and experimentally demonstrate a generalized active noise cancellation scheme for quantum devices operating at sub-Kelvin temperatures. Our approach compensates periodic environmental interference by dynamically injecting a phase-coherent anti-noise signal directly into the device. We employ an automated feedback protocol featuring beat-frequency reduction and adaptive phase-amplitude tuning, enabling real-time compensation without any manual intervention. Unlike post-processing or passive filtering, this method suppresses noise at the device level without introducing additional time constants. We implement the scheme on a gate-defined Si/SiGe quantum dot subject to strong 50 Hz powerline interference and validate its effectiveness through acquiring Coulomb Blockade Oscillations and Coulomb diamond plots. The technique achieves substantial suppression of both the targeted interference and the overall noise floor, thereby stabilizing transport characteristics and enhancing device fidelity. While demonstrated on a quantum dot, the proposed framework is broadly applicable to a wide class of solid-state quantum devices where deterministic noise presents a critical bottleneck.
Measuring electrical charge with single-electron resolution is essential for implementing spin qubits in semiconductor quantum dots. Applying quantum dot charge sensors in larger-scale quantum computing devices is being held back because these sensors are easily affected by environmental disturbances, and thus require elaborate tune-up. This study uses a feedback control circuit implemented on a field-programmable gate array to maintain sensor performance in the face of capacitive crosstalk and charge noise. The technique enables rapid tune-up of quantum dot devices and reliable single-shot electron or spin measurement, and will advance the development of large-scale spin-qubit devices.
Spins in semiconductor quantum dots offer a scalable approach to quantum computing; however, precise control and efficient readout of large quantum dot arrays remain challenging, mainly due to the hyperdimensional voltage space required for tuning multiple gates per dot. To automate this process, large datasets are required for testing and training autotuning algorithms. To address the demand for such large datasets, we introduce RF-Squad, a physics-based simulator designed to realistically replicate radiofrequency (RF) reflectometry measurements of quantum dot arrays, with the ability to go beyond the Constant Interaction Model (CIM) and simulate physical phenomena such as tunnel coupling, tunnel rates, and quantum confinement. Implemented in JAX, an accelerated linear algebra library, RF-Squad achieves high computational speed, enabling the simulation of a 100x100 pixel charge stability diagram of a double quantum dot (DQD) in 52.1 $\pm$0.2 milliseconds at the CIM level. Using optimization algorithms, combined with it's layered architecture, RF-Squad allows users to balance physical accuracy with computational speed, scaling from simple to highly detailed models.