arXiv subjects

Ross C. C. Leon

Publications and source records attributed to Ross C. C. Leon.

16 recordsLinked to original sources

arXiv2026

Radiofrequency cascade readout of coupled spin qubits

Silicon spin qubits based on metal-oxide-semiconductor (MOS) technology are compatible with semiconductor manufacturing and offer a route to scalable quantum processing. However, spin readout typically relies on proximal charge sensors, which add architectural complexity and limit qubit connectivity. In situ dispersive readout techniques are more compact, which can alleviate these constraints, but exhibit limited sensitivity. Here we report a radiofrequency electron-cascade readout method that enhances the dispersive signal through alternating-current electron co-tunnelling. With this approach, we achieve an enhancement in signal-to-noise ratio of more than $35~$dB, leading to a minimum integration time of $7.6 \pm 0.2~μ$s. We demonstrate singlet-triplet readout of two-electron spins in a natural silicon planar MOS quantum dot array, and coherent spin control using the exchange interaction, which forms the basis for entangling gates. We find dephasing times of up to $500~$ns and a gate quality factor that exceeds 10.

cond-mat.mes-hall↗

arXiv2026

A current source with metrological precision made on a 300mm silicon MOS process

Although the measurement of current is now defined with respect to the electronic charge, producing a current standard based on a single-electron source remains challenging. The error rate of a source must be below 0.01 ppm, and many such sources must be operated in parallel to provide practically useful values of current in the nanoampere range. Achieving a single electron source using an industrial grade 300 mm wafer silicon metal oxide semiconductor (MOS) process could offer a powerful route for scaling, combined with the ability for integration with control and measurement electronics. Here, we present measurements of such a single-electron source indicating an error rate of 0.008 ppm, below the error threshold to satisfy the SI Ampere, and one of the lowest error rates reported, implemented using a gate-defined quantum dot device fabricated on an industry-grade silicon MOS process. Further evidence supporting the accuracy of the device is obtained by comparing the device performance to established models of quantum tunnelling, which reveal the mechanism of operation of our source at the single particle level. The low error rate observed in this device motivates the development of scaled arrays of parallel sources utilising Si MOS devices to realise a new generation of metrologically accurate current standards.

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arXiv2025

Spin Readout in a 22 nm Node Integrated Circuit

Constructing a quantum computer capable of broad and important applications is likely to require millions of addressable physical qubits, posing the challenge of large-scale integration of quantum systems with classical electronics. Fully depleted silicon-on-insulator CMOS technology has been used to develop a range of cryogenic electronic components for the control and readout of different qubit modalities interfaced on separate chips. However, recent measurements of quantum dots on this technology raise the tantalising prospect of realising control electronics and spin qubits on the same manufacturing platform, within a single integrated circuit (IC). Here, we demonstrate single-shot spin readout in addressable quantum dot devices within an IC fabricated using industry-standard 22 nm fully depleted silicon-on-insulator technology. We achieve spin-to-charge conversion via a ramped energy-selective measurement, detected using a radio-frequency single-electron transistor and addressed by on-chip cryogenic electronics. The observation of consistent readout visibilities exceeding 90% and millisecond spin relaxation times in two nominally identical devices within the addressable array supports the reproducibility of the unit cell. The successful observation of spin readout using this CMOS process marks a key step towards realising highly scalable and integrated spin qubits.

quant-ph↗

arXiv2025

Enhancement of Electric Drive in Silicon Quantum Dots with Electric Quadrupole Spin Resonance

Quantum computation with electron spin qubits requires coherent and efficient manipulation of these spins, typically accomplished through the application of alternating magnetic or electric fields for electron spin resonance (ESR). In particular, electrical driving allows us to apply localized fields on the electrons, which benefits scale-up architectures. However, we have found that Electric Dipole Spin Resonance (EDSR) is insufficient for modeling the Rabi behavior in recent experimental studies. Therefore, we propose that the electron spin is being driven by a new method of electric spin qubit control which generalizes the spin dynamics by taking into account a quadrupolar contribution of the quantum dot: electric quadrupole spin resonance (EQSR). In this work, we explore the electric quadrupole driving of a quantum dot in silicon, specifically examining the cases of 5 and 13 electron occupancies.

cond-mat.mes-hall↗

arXiv2025

Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact

Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data. While classical machine learning (CML) approaches have been successful in many domains, their performance degrades in small-sample, nonlinear scenarios. In this work, we investigate quantum machine learning (QML) as an alternative, exploiting quantum kernels to capture intricate correlations from compact datasets. Using only 159 experimental GaN HEMT samples, we develop a quantum kernel-aligned regressor (QKAR) combining a shallow Pauli-Z feature map with a trainable quantum kernel alignment (QKA) layer. All models, including seven baseline CML regressors, are evaluated under a unified PCA-based preprocessing pipeline to ensure a fair comparison. QKAR consistently outperforms classical baselines across multiple metrics (MAE, MSE, RMSE), achieving a mean absolute error of 0.338 Omega mm when validated on experimental data. We further assess noise robustness and generalization through cross-validation and new device fabrication. These findings suggest that carefully constructed QML models could provide predictive advantages in data-constrained semiconductor modeling, offering a foundation for practical deployment on near-term quantum hardware. While challenges remain for both QML and CML, this study demonstrates QML's potential as a complementary approach in complex process modeling tasks.

cs.LG↗

arXiv2025

High-fidelity dispersive spin sensing in a tuneable unit cell of silicon MOS quantum dots

Metal-oxide-semiconductor (MOS) technology is a promising platform for developing quantum computers based on spin qubits. Scaling this approach will benefit from compact and sensitive sensors that minimize constraints on qubit connectivity while being industrially manufacturable. Here, we demonstrate a compact dispersive spin-qubit sensor, a single-electron box (SEB), within a bilinear unit cell of planar MOS quantum dots (QDs) fabricated using an industrial grade 300 mm wafer process. By independent gate control of the SEB and double-quantum-dot tunnel rates, we optimize the sensor to achieve a readout fidelity of 99.92% in 340us (99% in 20us), fidelity values on a par with the best obtained with less compact sensors. Furthermore, we develop a Hidden Markov Model of the two-electron spin dynamics that enables a more accurate calculation of the measurement outcome and hence readout fidelity. Our results show how high-fidelity sensors can be introduced within silicon spin-qubit architectures while maintaining sufficient qubit connectivity as well as providing faster readout and more efficient initialisation schemes.

quant-ph↗

arXiv2024

Bounds to electron spin qubit variability for scalable CMOS architectures

Spins of electrons in CMOS quantum dots combine exquisite quantum properties and scalable fabrication. In the age of quantum technology, however, the metrics that crowned Si/SiO2 as the microelectronics standard need to be reassessed with respect to their impact upon qubit performance. We chart the spin qubit variability due to the unavoidable atomic-scale roughness of the Si/SiO$_2$ interface, compiling experiments in 12 devices, and developing theoretical tools to analyse these results. Atomistic tight binding and path integral Monte Carlo methods are adapted for describing fluctuations in devices with millions of atoms by directly analysing their wavefunctions and electron paths instead of their energy spectra. We correlate the effect of roughness with the variability in qubit position, deformation, valley splitting, valley phase, spin-orbit coupling and exchange coupling. These variabilities are found to be bounded and lie within the tolerances for scalable architectures for quantum computing as long as robust control methods are incorporated.

quant-ph↗

arXiv2024

Assessment of error variation in high-fidelity two-qubit gates in silicon

Achieving high-fidelity entangling operations between qubits consistently is essential for the performance of multi-qubit systems and is a crucial factor in achieving fault-tolerant quantum processors. Solid-state platforms are particularly exposed to errors due to materials-induced variability between qubits, which leads to performance inconsistencies. Here we study the errors in a spin qubit processor, tying them to their physical origins. We leverage this knowledge to demonstrate consistent and repeatable operation with above 99% fidelity of two-qubit gates in the technologically important silicon metal-oxide-semiconductor (SiMOS) quantum dot platform. We undertake a detailed study of these operations by analysing the physical errors and fidelities in multiple devices through numerous trials and extended periods to ensure that we capture the variation and the most common error types. Physical error sources include the slow nuclear and electrical noise on single qubits and contextual noise. The identification of the noise sources can be used to maintain performance within tolerance as well as inform future device fabrication. Furthermore, we investigate the impact of qubit design, feedback systems, and robust gates on implementing scalable, high-fidelity control strategies. These results are achieved by using three different characterization methods, we measure entangling gate fidelities ranging from 96.8% to 99.8%. Our analysis tools identify the causes of qubit degradation and offer ways understand their physical mechanisms. These results highlight both the capabilities and challenges for the scaling up of silicon spin-based qubits into full-scale quantum processors.

quant-ph↗

arXiv2023

Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays

Quantum processors based on integrated nanoscale silicon spin qubits are a promising platform for highly scalable quantum computation. Current CMOS spin qubit processors consist of dense gate arrays to define the quantum dots, making them susceptible to crosstalk from capacitive coupling between a dot and its neighbouring gates. Small but sizeable spin-orbit interactions can transfer this electrostatic crosstalk to the spin g-factors, creating a dependence of the Larmor frequency on the electric field created by gate electrodes positioned even tens of nanometers apart. By studying the Stark shift from tens of spin qubits measured in nine different CMOS devices, we developed a theoretical frawework that explains how electric fields couple to the spin of the electrons in increasingly complex arrays, including those electric fluctuations that limit qubit dephasing times $T_2^*$. The results will aid in the design of robust strategies to scale CMOS quantum technology.

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arXiv2023

Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

cs.LG↗

arXiv2023

Control of dephasing in spin qubits during coherent transport in silicon

One of the key pathways towards scalability of spin-based quantum computing systems lies in achieving long-range interactions between electrons and increasing their inter-connectivity. Coherent spin transport is one of the most promising strategies to achieve this architectural advantage. Experimental results have previously demonstrated high fidelity transportation of spin qubits between two quantum dots in silicon and identified possible sources of error. In this theoretical study, we investigate these errors and analyze the impact of tunnel coupling, magnetic field and spin-orbit effects on the spin transfer process. The interplay between these effects gives rise to double dot configurations that include regimes of enhanced decoherence that should be avoided for quantum information processing. These conclusions permit us to extrapolate previous experimental conclusions and rationalize the future design of large scale quantum processors.

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arXiv2022

Jellybean quantum dots in silicon for qubit coupling and on-chip quantum chemistry

The small size and excellent integrability of silicon metal-oxide-semiconductor (SiMOS) quantum dot spin qubits make them an attractive system for mass-manufacturable, scaled-up quantum processors. Furthermore, classical control electronics can be integrated on-chip, in-between the qubits, if an architecture with sparse arrays of qubits is chosen. In such an architecture qubits are either transported across the chip via shuttling, or coupled via mediating quantum systems over short-to-intermediate distances. This paper investigates the charge and spin characteristics of an elongated quantum dot -- a so-called jellybean quantum dot -- for the prospects of acting as a qubit-qubit coupler. Charge transport, charge sensing and magneto-spectroscopy measurements are performed on a SiMOS quantum dot device at mK temperature, and compared to Hartree-Fock multi-electron simulations. At low electron occupancies where disorder effects and strong electron-electron interaction dominate over the electrostatic confinement potential, the data reveals the formation of three coupled dots, akin to a tunable, artificial molecule. One dot is formed centrally under the gate and two are formed at the edges. At high electron occupancies, these dots merge into one large dot with well-defined spin states, verifying that jellybean dots have the potential to be used as qubit couplers in future quantum computing architectures.

cond-mat.mes-hall↗

arXiv2022

On-demand electrical control of spin qubits

Once called a "classically non-describable two-valuedness" by Pauli , the electron spin is a natural resource for long-lived quantum information since it is mostly impervious to electric fluctuations and can be replicated in large arrays using silicon quantum dots, which offer high-fidelity control. Paradoxically, one of the most convenient control strategies is the integration of nanoscale magnets to artificially enhance the coupling between spins and electric field, which in turn hampers the spin's noise immunity and adds architectural complexity. Here we demonstrate a technique that enables a \emph{switchable} interaction between spins and orbital motion of electrons in silicon quantum dots, without the presence of a micromagnet. The naturally weak effects of the relativistic spin-orbit interaction in silicon are enhanced by more than three orders of magnitude by controlling the energy quantisation of electrons in the nanostructure, enhancing the orbital motion. Fast electrical control is demonstrated in multiple devices and electronic configurations, highlighting the utility of the technique. Using the electrical drive we achieve coherence time $T_{2,{\rm Hahn}}\approx50 μ$s, fast single-qubit gates with ${T_{π/2}=3}$ ns and gate fidelities of 99.93 % probed by randomised benchmarking. The higher gate speeds and better compatibility with CMOS manufacturing enabled by on-demand electric control improve the prospects for realising scalable silicon quantum processors.

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arXiv2021

A high-sensitivity charge sensor for silicon qubits above one kelvin

Recent studies of silicon spin qubits at temperatures above 1 K are encouraging demonstrations that the cooling requirements for solid-state quantum computing can be considerably relaxed. However, qubit readout mechanisms that rely on charge sensing with a single-island single-electron transistor (SISET) quickly lose sensitivity due to thermal broadening of the electron distribution in the reservoirs. Here we exploit the tunneling between two quantised states in a double-island SET (DISET) to demonstrate a charge sensor with an improvement in signal-to-noise by an order of magnitude compared to a standard SISET, and a single-shot charge readout fidelity above 99 % up to 8 K at a bandwidth > 100 kHz. These improvements are consistent with our theoretical modelling of the temperature-dependent current transport for both types of SETs. With minor additional hardware overheads, these sensors can be integrated into existing qubit architectures for high fidelity charge readout at few-kelvin temperatures.

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arXiv2021

Pauli Blockade in Silicon Quantum Dots with Spin-Orbit Control

Quantum computation relies on accurate measurements of qubits not only for reading the output of the calculation, but also to perform error correction. Most proposed scalable silicon architectures utilize Pauli blockade of triplet states for spin-to-charge conversion. In recent experiments, there have been instances when instead of conventional triplet blockade readout, Pauli blockade is sustained only between parallel spin configurations, with $|T_0\rangle$ relaxing quickly to the singlet state and leaving $|T_+\rangle$ and $|T_-\rangle$ states blockaded -- which we call \textit{parity readout}. Both types of blockade can be used for readout in quantum computing, but it is crucial to maximize the fidelity and understand in which regime the system operates. We devise and perform an experiment in which the crossover between parity and singlet-triplet readout can be identified by investigating the underlying physics of the $|T_0\rangle$ relaxation rate. This rate is tunable over four orders of magnitude by controlling the Zeeman energy difference between the dots induced by spin-orbit coupling, which in turn depends on the direction of the applied magnetic field. We suggest a theoretical model incorporating charge noise and relaxation effects that explains quantitatively our results. Investigating the model both analytically and numerically, we identify strategies to obtain on-demand either singlet-triplet or parity readout consistently across large arrays of dots. We also discuss how parity readout can be used to perform full two-qubit state tomography and its impact on quantum error detection schemes in large-scale silicon quantum computers.

quant-ph↗

arXiv2020

Bell-state tomography in a silicon many-electron artificial molecule

An error-corrected quantum processor will require millions of qubits, accentuating the advantage of nanoscale devices with small footprints, such as silicon quantum dots. However, as for every device with nanoscale dimensions, disorder at the atomic level is detrimental to qubit uniformity. Here we investigate two spin qubits confined in a silicon double-quantum-dot artificial molecule. Each quantum dot has a robust shell structure and, when operated at an occupancy of 5 or 13 electrons, has single spin-$\frac{1}{2}$ valence electron in its $p$- or $d$-orbital, respectively. These higher electron occupancies screen atomic-level disorder. The larger multielectron wavefunctions also enable significant overlap between neighbouring qubit electrons, while making space for an interstitial exchange-gate electrode. We implement a universal gate set using the magnetic field gradient of a micromagnet for electrically-driven single qubit gates, and a gate-voltage-controlled inter-dot barrier to perform two-qubit gates by pulsed exchange coupling. We use this gate set to demonstrate a Bell state preparation between multielectron qubits with fidelity 90.3%, confirmed by two-qubit state tomography using spin parity measurements.

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