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Tuning Methods for Semiconductor Spin Qubits

Tim Botzem, Michael Shulman, Arne Ludwig, Andreas Dirk Wieck, Dieter Schuh, Dominique Bougeard, Amir Yacoby, Hendrik Bluhm, Sandra Foletti, Shannon P. Harvey, Oliver Dial, Patrick Bethke, Pascal Cerfontaine, Robert P. G. McNeil, D. Mahalu, V. Umansky

Published Jan 1, 2018• 36 citations
OpenAlex

Abstract

We present efficient methods to reliably characterize and tune gate-defined semiconductor spin qubits. Our methods are developed for double quantum dots in GaAs heterostructures, but they can easily be adapted to other quantum-dot-based qubit systems. These tuning procedures include the characterization of the interdot tunnel coupling, the tunnel coupling to the surrounding leads, and the identification of various fast initialization points for the operation of the qubit. Since semiconductor-based spin qubits are compatible with standard semiconductor process technology and hence promise good prospects of scalability, the challenge of efficiently tuning the dot’s parameters will only grow in the near future, once the multiqubit stage is reached. With the anticipation of being used as the basis for future automated tuning protocols, all measurements presented here are fast-to-execute and easy-to-analyze characterization methods. They result in quantitative measures of the relevant qubit parameters within a couple of seconds and require almost no human interference.

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Tuning Methods for Semiconductor Spin Qubits

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Recent progress on increasingly complex devices based on semiconductor spin qubits shows that automated methods are indispensable for efficient device characterization and further scale-up. The authors present fast, fully automated procedures to extract key device parameters, as well as various readout and initialization points for operating a two-electron qubit. These efficient, quantitative methods can be the basis for automatic tune-up, in which gate voltages are iteratively adjusted based on the deviation of measured parameters from targets. This would enable operation of devices with more and more qubits, and systematically optimize fabrication procedures.

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While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as a function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3–5 iterations, corresponding to 10–15 min of lab-time.

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Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.

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A. R. Mills, M. M. Feldman, C. Monical +4 more

As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a “tunnel coupling lever arm” to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.

arXiv:1907.10775•Applied Physics Letters•Sep 9, 2019•59 citations
We present efficient methods to reliably characterize and tune gate-defined semiconductor spin qubits. Our methods are developed for double quantum dots in GaAs heterostructures, but they can easily be adapted to other quantum-dot-based qubit systems. These tuning procedures include the characterization of the interdot tunnel coupling, the tunnel coupling to the surrounding leads, and the identification of various fast initialization points for the operation of the qubit. Since semiconductor-based spin qubits are compatible with standard semiconductor process technology and hence promise good prospects of scalability, the challenge of efficiently tuning the dot’s parameters will only grow in the near future, once the multiqubit stage is reached. With the anticipation of being used as the basis for future automated tuning protocols, all measurements presented here are fast-to-execute and easy-to-analyze characterization methods. They result in quantitative measures of the relevant qubit parameters within a couple of seconds and require almost no human interference.
Recent progress on increasingly complex devices based on semiconductor spin qubits shows that automated methods are indispensable for efficient device characterization and further scale-up. The authors present fast, fully automated procedures to extract key device parameters, as well as various readout and initialization points for operating a two-electron qubit. These efficient, quantitative methods can be the basis for automatic tune-up, in which gate voltages are iteratively adjusted based on the deviation of measured parameters from targets. This would enable operation of devices with more and more qubits, and systematically optimize fabrication procedures.
While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as a function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3–5 iterations, corresponding to 10–15 min of lab-time.
Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.
Spin qubits in semiconductor quantum circuits have been identified as a compelling platform for large scale quantum computing. Today an ongoing effort of the community is aiming at the scaling of such systems by filling larger and larger arrays. In this context the development of tuning and readout protocols allowing to reduce the inherent complexity of such systems seems to be a necessity.In the first part of my thesis, I demonstrate array partitioning of a linear triple quantum dot defined in a AlGaAs/GaAs heterostructure via a highly tunable interdot tunnel coupling. This allows us to operate independently smaller sections of the array which reduces the number of available charge states for the system and therefore reduces its complexity during the tuning.The second part of my thesis is focused on reducing the hardware overhead necessary to extract information during the readout of the spin qubits. In semiconductor spin qubits, high fidelity read-out is obtained through Pauli spin blockade in a double quantum dot with two electrons probed by a local electrometer. One electron is the qubit, the second is the ancilla and only one bit of information is extracted. To obtain the full two-bit information of the two-electron spin states, more quantum hardware is necessary (either dots, electrometers, or reservoirs).Based on an original protocol, I develop in this thesis a complete single-shot read-out procedure for two-electron spin states in a double quantum dot discriminating between S, T0, T+ and T-. It uses successively three distinct spin-to-charge conversion protocols interleaved with charge configuration measurements. The strategy is based on four novel experimental achievements:First, the control from the GHz to sub-Hz regime of the interdot tunnel coupling at the nanosecond timescale.Second, the separation of the spin-to-charge conversion and the charge read-out in the voltage gate space.Third, the suppression of spin relaxation process during the read-out procedure.Finally, the repetition of three distinct spin-to-charge conversion protocols.The read-out procedure is tested on various initialized populations and implemented in more complex spin manipulation sequences to assess its validity.
Gate-defined semiconductor quantum dots utilize fast electrical control to manipulate spin and charge states of individual electrons. Electrical pulse distortions can limit control fidelities but are difficult to measure at the device level. Here, we use detuning-axis pulsed spectroscopy to characterize baseband pulse distortions in a silicon double quantum-dot. We extract the gate-voltage impulse response and apply a digital pre-distortion filter to eliminate pulse distortions on timescales longer than 1~ns. With the pre-distortion, we reduce the frequency chirp of coherent exchange oscillations in a singlet-triplet qubit. Our results suggest a scalable and tuning-efficient method for characterizing pulse distortions in quantum-dot spin qubits.
As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a “tunnel coupling lever arm” to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.