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Coherent manipulation of valley states at multiple charge configurations of a silicon quantum dot device.

ABSTRACT: Qubits based on silicon quantum dots are emerging as leading candidates for the solid-state implementation of quantum information processing. In silicon, valley states represent a degree of freedom in addition to spin and charge. Characterizing and controlling valley states is critical for the encoding and read-out of electrons-in-silicon-based qubits. Here, we report the coherent manipulation of a qubit, which is based on the two valley states of an electron confined in a silicon quantum dot. We carry out valley qubit operations at multiple charge configurations of the double quantum dot device. The dependence of coherent oscillations on pulse excitation level and duration allows us to map out the energy dispersion as a function of detuning as well as the phase coherence time of the valley qubit. The coherent manipulation also provides a method of measuring valley splittings that are too small to probe with conventional methods.Silicon quantum dots provide a promising platform for quantum computing based on manipulation of electron degrees of freedom in a well-characterized environment. Here, the authors demonstrate coherent control of electron valley states, yielding an accurate determination of the valley splitting.

SUBMITTER: Schoenfield JS

PROVIDER: S-EPMC5498670 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Coherent manipulation of valley states at multiple charge configurations of a silicon quantum dot device.

Schoenfield Joshua S JS Freeman Blake M BM Jiang HongWen H

Nature communications 20170705 1

Qubits based on silicon quantum dots are emerging as leading candidates for the solid-state implementation of quantum information processing. In silicon, valley states represent a degree of freedom in addition to spin and charge. Characterizing and controlling valley states is critical for the encoding and read-out of electrons-in-silicon-based qubits. Here, we report the coherent manipulation of a qubit, which is based on the two valley states of an electron confined in a silicon quantum dot. W ...[more]

PMID: 28680042

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Project description:BackgroundOver the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.Materials and methodsTo implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.Results and discussionFrom 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ? 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.

Dataset Information

Coherent manipulation of valley states at multiple charge configurations of a silicon quantum dot device.

Publications

Coherent manipulation of valley states at multiple charge configurations of a silicon quantum dot device.

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