arXiv subjects

Jared Benson

Publications and source records attributed to Jared Benson.

7 recordsLinked to original sources

arXiv2026

Individually tunable Si/SiGe quantum dot operating voltages via gate-biased illumination

Semiconductor quantum dot qubits often require very different voltages on each gate to bring them to a correct operating point. Here, we present a method by which one can controllably and repeatably alter the nanoscale trapped charge distribution at an oxide-semiconductor interface. We demonstrate this method on a Si/SiGe quantum dot device, and we find that the operating voltages can be controlled and made much more uniform. The method relies on illumination with near-infrared light in the presence of applied gate voltages, and it enables the tuning of the device operating point on a gate-by-gate basis. We present an explanation of the underlying physics using self-consistent Schr\"odinger-Poisson simulations. As an application of this method, we tune a triple quantum dot to have uniform and small operating voltages in the (1,1,1) charge configuration. Importantly, we show that shifting the operating voltages in this way does not change the measured charge noise.

cond-mat.mes-hall↗

arXiv2026

Large quantum dot energy level shifts in anomalous photon-assisted tunneling

Orbital energy splittings are important quantum dot parameters for the operation of hole spin qubits. They are known to depend on the lateral confinement of the quantum dots. However, when changing top, plunger gate voltages, which are the typical control parameter for qubit applications, such energy splitting changes are typically negligible, both as measured in experiment and as assumed in effective theories. Here, we study the singlet-triplet (ST) splittings, which depend on the orbital splittings, of a double quantum dot (DQD) in a Ge/SiGe heterostructure using photon-assisted tunneling (PAT) and pulsed-gate spectroscopy. We find that the ST splittings have a surprising, strong dependence on the top gate voltages, leading to anomalous PAT measurements. We combine data from both measurements in a model that well describes the linear gate-voltage dependence of the ST splittings. Finally, we show that the ST splittings of the two dots exhibit similar linear gate-voltage dependences when the device is retuned such that their ratio is significantly different.

cond-mat.mes-hall↗

arXiv2025

Fast high-fidelity baseband reset of a latched state for quantum dot qubit readout

A common method for reading out the state of a spin qubit is by latching one logical qubit state, either $|1\rangle$ or $|0\rangle$, onto a different, metastable charge state. Such a latched state can provide a superior charge sensing signal for qubit readout, and it can have a lifetime chosen to be long enough that the charge sensed readout can be high fidelity. However, the passive reset out of latched states is inherently long, which is not desirable. In this work, we demonstrate an on-demand, high fidelity (> 99%) re-initialization of a quantum dot qubit out of a latched readout state. The method is simple to apply as it involves a single baseband voltage pulse to a specific region in the quantum dot stability diagram where the relaxation time from the latched state to the ground state is over 50 times faster. We describe the mechanism for the reset process as well as the boundaries for the optimal reset region in the qubit gate voltage space.

cond-mat.mes-hall↗

arXiv2024

Bootstrapping, autonomous testing, and initialization system for Si/Si$_x$Ge$_{1-x}$ multi-quantum-dot devices

Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with a standard deviation of approximately 83 mV of variation within state-of-the-art present-day devices. Efficient characterization and tuning of large arrays of QD qubits depend on choices of automated protocols. Here, we introduce a physically intuitive framework for a bootstrapping, autonomous testing, and initialization system (BATIS) designed to streamline QD device evaluation and calibration. BATIS navigates high-dimensional gate voltage spaces, automating essential steps such as leakage testing, formation of all current channels, and gate characterization in the presence of trapped charges. For forming the current channels, BATIS follows a non-standard approach that requires a single set of measurements regardless of the number of channels. Demonstrated at 1.3 K on a quad-QD Si/Si$_x$Ge$_{1-x}$ device, BATIS eliminates the need for deep cryogenic environments during initial device diagnostics, significantly enhancing scalability and reducing setup times. By requiring only minimal prior knowledge of the device architecture, BATIS represents a platform-agnostic solution, adaptable to various QD systems, which bridges a critical gap in QD autotuning.

cond-mat.mes-hall↗

arXiv2024

Single-shot latched readout of a quantum dot qubit using barrier gate pulsing

Latching techniques are widely used to enhance readout of qubits. These methods require precise tuning of multiple tunnel rates, which can be challenging to achieve under realistic experimental conditions, such as when a qubit is coupled to a single reservoir. Here, we present a method for single-shot measurement of a quantum dot qubit with a single reservoir using a latched-readout scheme. Our approach involves pulsing a barrier gate to dynamically control qubit-to-reservoir tunnel rates, a method that is readily applicable to the latched readout of various spin-based qubits. We use this method to enable qubit state latching and to reduce the qubit reset time in measurements of coherent Larmor oscillations of a Si/SiGe quantum dot hybrid qubit.

cond-mat.mes-hall↗

arXiv2024

Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning

The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called $\textit{triangle plots}$, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is $\textit{good}$ and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of $\textit{bad}$ images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug $\textit{et al.}$ 2024 $\textit{Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada)}$ pp 1-6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy.

cs.CV↗

arXiv2023

Data needs and challenges for quantum dot devices automation

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

cond-mat.mes-hall↗