arXiv subjects

Thomas McJunkin

Publications and source records attributed to Thomas McJunkin.

At least 19 recordsLinked to original sources

arXiv2026

Characterization of Radiation-Induced Errors in Superconducting Qubits Protected with Various Gap-Engineering Strategies

Impacts from high-energy particles cause correlated errors in superconducting qubits by increasing the quasiparticle density in the vicinity of the Josephson junctions (JJs). Such errors are particularly harmful as they cannot be easily remedied via conventional error correcting codes. Recent experiments reduced correlated errors by making the difference in superconducting gap energy across the JJ larger than the qubit transition energy. In this work, we assess gap engineering near the JJ ($δΔ_{\mathrm{JJ}}$) and the capacitor/ground-plane ($δΔ_{\mathrm{M1}}$) by exposing arrays of transmon qubits to two sources of radiation. For $α$-particles from an $^{241}$Am source, we observe $T_1$ errors correlated in space and time, supporting a hypothesis that hadronic cosmic rays are a major contributor to the $10^{-10}$ error floor observed in Ref. 1. For electrons from a pulsed linear accelerator, we observe temporally correlated $T_1$ and $T_2$ errors, this measurement is insensitive to spatial correlations. We observe that the severity of correlated $T_1$ errors is reduced for qubit arrays with a greater degree of gap engineering at the JJ. For both $T_1$ and $T_2$ errors, the recovery time is hastened by an increased $δΔ_{\mathrm{M1}}$, which we attribute to the trapping of quasiparticles into the capacitor/ground-plane. We construct a model of quasiparticle dynamics that qualitatively agrees with our observations. This work reinforces the multifaceted influence of radiation on superconducting qubits and provides strategies for improving radiation resilience.

quant-ph↗

arXiv2026

On-Demand Correlated Errors in Superconducting Qubits from a Particle Accelerator

Ionizing radiation is a known source of correlated errors in superconducting quantum processors, inhibiting the functionality of quantum error correction surface codes. High-energy photons and charged particles deposit pair-breaking energy into these systems leading to excess quasiparticles near Josephson junctions that increase qubit decoherence. Previous investigations of this problem have relied on ambient, stochastic sources of ionizing radiation or alternative methods of quasiparticle generation. Here, we present a facility that couples an electron linear accelerator (linac) to a dilution refrigerator to study ionizing radiation in quantum systems. A single linac electron closely mimics the energy deposition characteristics of a typical cosmic-ray muon, and we demonstrate the facility's usefulness with a multi-qubit superconducting transmon chip. Characteristic radiation-induced relaxation errors are quickly and easily collected with the speed and timing information of the linac. Additionally, we present qubit excitation and detuning errors that can be difficult to detect without the on-demand source of ionizing radiation. These error signatures are shown to be dependent on the junction placement and surrounding superconducting gaps.

quant-ph↗

arXiv2024

On-chip cryogenic multiplexing of Si/SiGe quantum devices

The challenges of operating qubits in a cryogenic environment point to a looming bottleneck for large-scale quantum processors, limited by the number of input-output connections. Classical processors solve this problem via multiplexing; however, on-chip multiplexing circuits have not been shown to have similar benefits for cryogenic quantum devices. In this work we integrate classical circuitry and Si/SiGe quantum devices on the same chip, providing a test bed for qubit scale-up. Our method uses on-chip field-effect transistors (FETs) to multiplex a grid of work zones, achieving a nearly tenfold reduction in control wiring. We leverage this set-up to probe device properties across a 6x6mm$^2$ array of 16 Hall bars. We successfully operate the array at cryogenic temperatures and high magnetic fields where the quantum Hall effect is observed. Building upon these results, we propose a vision for readout in a large-scale silicon quantum processor with a limited number of control connections.

cond-mat.mes-hall↗

arXiv2024

Control of threshold voltages in Si/SiGe quantum devices via optical illumination

Optical illumination of quantum-dot qubit devices at cryogenic temperatures, while not well studied, is often used to recover operating conditions after undesired shocking events or charge injection. Here, we demonstrate systematic threshold voltage shifts in a dopant-free, Si/SiGe field effect transistor using a near infrared (780 nm) laser diode. We find that illumination under an applied gate voltage can be used to set a specific, stable, and reproducible threshold voltage that, over a wide range in gate bias, is equal to that gate bias. Outside this range, the threshold voltage can still be tuned, although the resulting threshold voltage is no longer equal to the applied gate bias during illumination. We present a simple and intuitive model that provides a mechanism for the tunability in gate bias. The model presented also explains why cryogenic illumination is successful at resetting quantum dot qubit devices after undesired charging events.

cond-mat.mes-hall↗

arXiv2022

SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits

Large-scale arrays of quantum-dot spin qubits in Si/SiGe quantum wells require large or tunable energy splittings of the valley states associated with degenerate conduction band minima. Existing proposals to deterministically enhance the valley splitting rely on sharp interfaces or modifications in the quantum well barriers that can be difficult to grow. Here, we propose and demonstrate a new heterostructure, the "Wiggle Well," whose key feature is Ge concentration oscillations inside the quantum well. Experimentally, we show that placing Ge in the quantum well does not significantly impact our ability to form and manipulate single-electron quantum dots. We further observe large and widely tunable valley splittings, from 54 to 239 ueV. Tight-binding calculations, and the tunability of the valley splitting, indicate that these results can mainly be attributed to random concentration fluctuations that are amplified by the presence of Ge alloy in the heterostructure, as opposed to a deterministic enhancement due to the concentration oscillations. Quantitative predictions for several other heterostructures point to the Wiggle Well as a robust method for reliably enhancing the valley splitting in future qubit devices.

quant-ph↗

arXiv2022

Latched readout for the quantum dot hybrid qubit

A primary method of reading out a quantum dot hybrid qubit involves projection of the logical basis onto distinct charge states that are readily detected by an integrated charge sensing dot. However, in the simplest configuration, the excited charge state decays rapidly, making single-shot readout challenging. Here, we demonstrate a readout procedure where the qubit excited state is latched to a metastable charge configuration whose lifetime is tunnel rate limited, persisting here as long as 2.5 ms. Additionally, we show that working in the (4,1)-(3,2) charge configuration enables a latched readout window that is larger and more tunable than typical charge configurations, because the size of the readout window is determined by an orbital splitting rather than a valley splitting.

cond-mat.mes-hall↗

arXiv2022

Toward Robust Autotuning of Noisy Quantum Dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a "gatekeeper" system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

quant-ph↗

arXiv2022

How valley-orbit states in silicon quantum dots probe quantum well interfaces

The energies of valley-orbit states in silicon quantum dots are determined by an as yet poorly understood interplay between interface roughness, orbital confinement, and electron interactions. Here, we report measurements of one- and two-electron valley-orbit state energies as the dot potential is modified by changing gate voltages, and we calculate these same energies using full configuration interaction calculations. The results enable an understanding of the interplay between the physical contributions and enable a new probe of the quantum well interface.

cond-mat.mes-hall↗

arXiv2022

Ray-based classification framework for high-dimensional data

While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than using dense, multi-dimensional data, we propose a deep neural network (DNN) classification framework that utilizes a minimal collection of one-dimensional representations, called \emph{rays}, to construct the "fingerprint" of the structure(s) based on substantially reduced information. We empirically study this framework using a synthetic dataset of double and triple quantum dot devices and apply it to the classification problem of identifying the device state. We show that the performance of the ray-based classifier is already on par with traditional 2D images for low dimensional systems, while significantly cutting down the data acquisition cost.

cs.LG↗

arXiv2022

Theoretical bounds on data requirements for the ray-based classification

The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes (Zwolak et al. in Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020], arXiv:2010.00500, 2020) and, more recently, has also been validated experimentally (Zwolak et al., PRX Quantum 2:020335, 2021). Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape's length, diameter, and exterior angles. For convex polytopes in $\mathbb{R}^N$, we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches.

cs.LG↗

arXiv2021

Ray-based framework for state identification in quantum dot devices

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the ``ray-based classification (RBC) framework,'' we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of image-based classification techniques from prior work while reducing the number of measurement points needed by up to 70 %. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs.This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.

quant-ph↗

arXiv2021

Valley splittings in Si/SiGe quantum dots with a germanium spike in the silicon well

Silicon-germanium heterostructures have successfully hosted quantum dot qubits, but the intrinsic near-degeneracy of the two lowest valley states poses an obstacle to high fidelity quantum computing. We present a modification to the Si/SiGe heterostructure by the inclusion of a spike in germanium concentration within the quantum well in order to increase the valley splitting. The heterostructure is grown by chemical vapor deposition and magnetospectroscopy is performed on gate-defined quantum dots to measure the excited state spectrum. We demonstrate a large and widely tunable valley splitting as a function of applied vertical electric field and lateral dot confinement. We further investigate the role of the germanium spike by means of tight-binding simulations in single-electron dots and show a robust doubling of the valley splitting when the spike is present, as compared to a standard (spike-free) heterostructure. This doubling effect is nearly independent of the electric field, germanium content of the spike, and spike location. This experimental evidence of a stable, tunable quantum dot, despite a drastic change to the heterostructure, provides a foundation for future heterostructure modifications.

cond-mat.mes-hall↗

arXiv2020

Coherent control and spectroscopy of a semiconductor quantum dot Wigner molecule

Multi-electron semiconductor quantum dots have found wide application in qubits, where they enable readout and enhance polarizability. However, coherent control in such dots has typically been restricted to only the lowest two levels, and such control in the strongly interacting regime has not been realized. Here we report quantum control of eight different resonances in a silicon-based quantum dot. We use qubit readout to perform spectroscopy, revealing a dense set of energy levels with characteristic spacing far smaller than the single-particle energy. By comparing with full configuration interaction calculations, we argue that the dense set of levels arises from Wigner-molecule physics.

cond-mat.mes-hall↗

arXiv2020

Fabrication process and failure analysis for robust quantum dots in silicon

We present an improved fabrication process for overlapping aluminum gate quantum dot devices on Si/SiGe heterostructures that incorporates low-temperature inter-gate oxidation, thermal annealing of gate oxide, on-chip electrostatic discharge (ESD) protection, and an optimized interconnect process for thermal budget considerations. This process reduces gate-to-gate leakage, damage from ESD, dewetting of aluminum, and formation of undesired alloys in device interconnects. Additionally, cross-sectional scanning transmission electron microscopy (STEM) images elucidate gate electrode morphology in the active region as device geometry is varied. We show that overlapping aluminum gate layers homogeneously conform to the topology beneath them, independent of gate geometry, and identify critical dimensions in the gate geometry where pattern transfer becomes non-ideal, causing device failure.

physics.app-ph↗

arXiv2020

The effect of external electric fields on silicon with superconducting gallium nano-precipitates

Motivated by potential transformative applications of nanoelectronic circuits that incorporate superconducting elements, and by the advantages of integrating these elements in a silicon materials platform, we investigate the properties of the superconductivity of silicon ion-implanted with gallium. Here we measure 40 different samples and explore both a variety of preparation methods (yielding both superconducting and non-superconducting samples), and the reproducibility of one of the preparation methods yielding superconducting samples. While we find agreement with the existing literature that superconducting effects are visible in this system, we also find that this superconductivity is not influenced by voltages applied to a top gate. The superconductivity in this materials system is not gateable for applied electric fields as large as 8 MV/cm. We also present results of scanning transmission electron microscopy imaging of some of the same samples for which we report electronic characterization. In agreement with the existing literature, we find that the presence of Ga precipitates is essential to the presence of a superconducting transition in these samples. However, we also find evidence for large inhomogeneities in this system, which we discuss in connection with the lack of gateability we report here.

physics.app-ph↗

arXiv2020

Auto-tuning of double dot devices in situ with machine learning

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the {\it in situ} implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

quant-ph↗

arXiv2019

Measurements of capacitive coupling within a quadruple quantum dot array

We present measurements of the capacitive coupling energy and the inter-dot capacitances in a linear quadruple quantum dot array in undoped Si/SiGe. With the device tuned to a regime of strong ($>$1 GHz) intra-double dot tunnel coupling, as is typical for double dot qubits, we measure a capacitive coupling energy of $20.9 \pm 0.3$ GHz. In this regime, we demonstrate a fitting procedure to extract all the parameters in the 4D Hamiltonian for two capacitively coupled charge qubits from a 2D slice through the quadruple dot charge stability diagram. We also investigate the tunability of the capacitive coupling energy, using inter-dot barrier gate voltages to tune the inter- and intra-double dot capacitances, and change the capacitive coupling energy of the double dots over a range of 15-32 GHz. We provide a model for the capacitive coupling energy based on the electrostatics of a network of charge nodes joined by capacitors, which shows how the coupling energy should depend on inter-double dot and intra-double dot capacitances in the network, and find that the expected trends agree well with the measurements of coupling energy.

quant-ph↗

arXiv2018

The critical role of substrate disorder in valley splitting in Si quantum wells

Motivated by theoretical predictions that spatially complex concentration modulations of Si and Ge can increase the valley splitting in quantum wells, we grow and characterize Si/SiGe heterostructures with a thin, pure Ge layer at the top of the quantum well using chemical vapor deposition. We show that these heterostructures remain hosts for high-mobility electron gases. We measure two quantum wells with approximately five monolayers of pure Ge at the upper barrier, finding mobilities as high as 70,000 cm$^2$/Vs, compared to 100,000 cm$^2$/Vs measured in samples with no Ge layer. Activation energy measurements in quantum Hall states corresponding to Fermi levels in the gap between different valley states reveal energy gaps ranging from 30 to over 200 $μ$eV, and we extract a surprisingly strong dependence of the energy gap on electron density. We interpret our results using tight binding theory and argue that our results are evidence that atomic scale disorder at the quantum well interface dominates the behavior of the valley splittings of these modified heterostructures.

cond-mat.mes-hall↗