Identifying single electron charge sensor events using wavelet edge detection

Prance, Jonathan; Bael, B. J. Van; Simmons, C. B.; Savage, D. E.; Lagally, M. G.; Friesen, Mark; Coppersmith, S. N.; Eriksson, M. A.

doi:10.1088/0957-4484/26/21/215201

Cited by 16 publications

(19 citation statements)

References 43 publications

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“…Figure S5 shows the application of a wavelet edge analysis algorithm to data of Fig. 4, allowing automated detection of single-electron-tunneling events as outlined by Prance et al (29 ) The technique is based on Canny's edge detection algorithm, used for the recognition of edges in images, and is well suited to detect sharp edges associated with sensor signals. In order to obtain the function W(t,s), the signal V H (black trace in S5a) is convolved with a scaled mother wavelet, namely the derivative of a Gaussian function of first order, for different scaling factors s of the wavelet function.…”

Section: Materials Characterization and Device Fabricationmentioning

confidence: 99%

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Fast Charge Sensing of Si/SiGe Quantum Dots via a High-Frequency Accumulation Gate

et al. 2019

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Quantum dot arrays are a versatile platform for the implementation of spin qubits, as highbandwidth sensor dots can be integrated with single-, double-and triple-dot qubits yielding fast and high-fidelity qubit readout. However, for undoped silicon devices, reflectometry off sensor ohmics suffers from the finite resistivity of the two-dimensional electron gas (2DEG), and alternative readout methods are limited to measuring qubit capacitance, rather than qubit charge. By coupling a surface-mount resonant circuit to the plunger gate of a high-impedance sensor, we realized a fast charge sensing technique that is compatible with resistive 2DEGs. We demonstrate this by acquiring at high speed charge stability diagrams of double-and tripledot arrays in Si/SiGe heterostructures as well as pulsed-gate single-shot charge and spin readout with integration times as low as 2.4 µs.

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Section: Materials Characterization and Device Fabricationmentioning

confidence: 99%

“…An alternative technique, which has been found to be more robust against lowfrequency noise and signal drift, is based on wavelet edge detection. (29 ) An example of such a wavelet analysis is shown in Supplementary Fig. S6.…”

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confidence: 99%

Fast Charge Sensing of Si/SiGe Quantum Dots via a High-Frequency Accumulation Gate

et al. 2019

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“…However, we can include this partial information in the likelihood function as well, which is particularly useful for data sets with relatively few state transitions and thus a higher fraction of censored dwell times. We emphasize that this maximum likelihood method can be applied to dwell times extracted using any technique; for example, it can be used with dwell times determined from wavelet analysis, which is more robust than thresholding for signals with substantial low-frequency drifts [44]. Details on the maximum likelihood estimation and functional forms of the likelihood function are provided in appendix D.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Quantum Zeno effect in the strong measurement regime of circuit quantum electrodynamics

et al. 2016

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We observe the quantum Zeno effect-where the act of measurement slows the rate of quantum state transitions-in a superconducting qubit using linear circuit quantum electrodynamics readout and a near-quantum-limited following amplifier. Under simultaneous strong measurement and qubit drive, the qubit undergoes a series of quantum jumps between states. These jumps are visible in the experimental measurement record and are analyzed using maximum likelihood estimation to determine qubit transition rates. The observed rates agree with both analytical predictions and numerical simulations. The analysis methods are suitable for processing general noisy random telegraph signals.

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“…For the latter, the analog measurement signal is often post-processed by peak-signal filters to assign a binary qubit readout. Examples for peak-signal filter are wavelet edge detection 9 , signal threshold 1 , 5 and slope threshold after filtering the signal with total variation denoising 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

Robust and fast post-processing of single-shot spin qubit detection events with a neural network

Struck

Lindner

Hollmann

et al. 2021

Sci Rep

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Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.

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Identifying single electron charge sensor events using wavelet edge detection

Cited by 16 publications

References 43 publications

Fast Charge Sensing of Si/SiGe Quantum Dots via a High-Frequency Accumulation Gate

Fast Charge Sensing of Si/SiGe Quantum Dots via a High-Frequency Accumulation Gate

Quantum Zeno effect in the strong measurement regime of circuit quantum electrodynamics

Robust and fast post-processing of single-shot spin qubit detection events with a neural network

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