Rapid Detection of Coherent Tunneling in an 
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 Nanowire Quantum Dot through Dispersive Gate Sensing

Radio-frequency (RF) reflectometry is implemented in hybrid semiconductor-superconductor nanowire systems designed to probe Majorana zero modes. Two approaches are presented. In the first, hybrid nanowire-based devices are part of a resonant circuit, allowing conductance to be measured as a function of several gate voltages ∼40 times faster than using conventional lowfrequency lock-in methods. In the second, nanowire devices are capacitively coupled to a nearby RF single-electron transistor made from a separate nanowire, allowing RF detection of charge, including charge-only measurement of the crossover from 2e inter-island charge transitions at zero magnetic field to 1e transitions at axial magnetic fields above 0.6 T, where a topological state is expected. Single-electron sensing yields signal-to-noise exceeding 3 and visibility 99.8% for a measurement time of 1 µs. arXiv:1902.00789v1 [cond-mat.mes-hall]

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“…We find V (1 µs) = 0.998. These results are comparable or better than previously reported charge detection studies [38][39][40][41][42].…”

Section: Fast Charge Measurement and Signal-to-noise Ratios In 1e supporting

confidence: 91%

Radio-Frequency Methods for Majorana-Based Quantum Devices: Fast Charge Sensing and Phase-Diagram Mapping

et al. 2019

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“…The resulting capacitance difference between the two qubit states can be monitored via a radio-frequency (RF) resonator bonded to one of the quantum dot electrodes. Similar dispersive shifts also occur at charge transitions in the quantum dots, such that the reflected signal assists with tuning to the desired electron occupation [14][15][16]. Dispersive readout has the advantage that it does not require a separate charge sensor, but often the capacitance sensitivity is insufficient for single-shot qubit readout even in systems with a long spin decay time [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 98%

Sensitive radiofrequency readout of quantum dots using an ultra-low-noise SQUID amplifier

Schupp

Vigneau

Wen

et al. 2020

Journal of Applied Physics

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Fault-tolerant spin-based quantum computers will require fast and accurate qubit readout. This can be achieved using radio-frequency reflectometry given sufficient sensitivity to the change in quantum capacitance associated with the qubit states. Here, we demonstrate a 23-fold improvement in capacitance sensitivity by supplementing a cryogenic semiconductor amplifier with a SQUID preamplifier. The SQUID amplifier operates at a frequency near 200 MHz and achieves a noise temperature below 600 mK when integrated into a reflectometry circuit, which is within a factor 120 of the quantum limit. It enables a record sensitivity to capacitance of 0.07 aF/ √ Hz. The setup is used to acquire charge stability diagrams of a gate-defined double quantum dot in a short time with a signal-to-noise ration of about 38 in 1 µs of integration time.

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“…The measurement time remains, however, the dominant contribution in the time required to identify transport features. Fast readout techniques such as radiofrequency reflectometry can be used to reduce measurement times [53][54][55][56][57][58] . However, these techniques are better suited to the measurement of small gate voltage windows.…”

Section: Discussionmentioning

confidence: 99%

Deep reinforcement learning for efficient measurement of quantum devices

et al. 2021

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Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

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Rapid Detection of Coherent Tunneling in an InAs Nanowire Quantum Dot through Dispersive Gate Sensing

Cited by 40 publications

References 30 publications

Radio-Frequency Methods for Majorana-Based Quantum Devices: Fast Charge Sensing and Phase-Diagram Mapping

Radio-Frequency Methods for Majorana-Based Quantum Devices: Fast Charge Sensing and Phase-Diagram Mapping

Sensitive radiofrequency readout of quantum dots using an ultra-low-noise SQUID amplifier

Deep reinforcement learning for efficient measurement of quantum devices

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