Computer-automated tuning procedures for semiconductor quantum dot arrays

Abstract

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 GHz values that are compatible with exchange gates.Quantum processors rely on classical hardware and controls in order to prepare, manipulate, and measure qubit states. For this reason, it is advantageous to develop tools to automate the operation of small quantum processors and routinely tune-up single qubit and two-qubit gates to maintain high performance 1-3 . Semiconductor spin qubits are a promising platform for realizing quantum computation largely due to their potential for scaling 4 . To tune up semiconductor quantum dots for operation as spin qubits requires control over the ground state charge occupation and chemical potential of each dot, as well as the interdot tunnel couplings 5 .Following the recent progress in constructing high-fidelity single-qubit and two-qubit gate operations with electron spins 6-11 , there are increasing efforts towards scaling to larger multi-qubit devices 12-15 . One of the key challenges in scaling up spin qubits is developing the software tools necessary to keep pace with increasingly complex devices. To date, approaches to implementing automated control software during tune-up of semiconductor qubits include training neural networks to identify the state of a device 16 , experimentally realizing automated control procedures for tuning double quantum dot (DQD) devices into the single-electron regime 17 , and automatically tuning the interdot tunnel coupling in a DQD [18][19][20] .In this Letter, we use an image analysis toolbox developed at Sandia National Laboratories to accurately analyze charge stability diagrams acquired from a triple quantum dot (TQD) unit cell of a 9-dot linear array 13,21 . Computer automated analysis of charge stability diagrams performs the inversion of the device capacitance matrix and the establishment of 'virtual gates'. Virtual gates compensate for cross-capacitances in the device and allow the chemical potential of each dot in the array to be independently controlled 13,22,23 . Furthermore, we use image analysis to locate interdot charge transitions and automatically perform measurements of the interdot tunnel coupling 24 . Using simple feedback protocols, we demonstrate simultaneous tune-up of the i...

Coherent Multispin Exchange Coupling in a Quantum-Dot Spin Chain

“…Two extra quantum dots placed above the main-dot array serve as charge sensors and are configured for rf reflectometry [37,38]. We tune the confinement potential using "virtual gates" [26,30,[39][40][41] such that each dot contains only one electron. We define virtual-plunger-gate voltages P 1 , P 2 , P 3 , and P 4 as linear combinations of the physicalplunger-gate voltages (p 1 , p 2 , p 3 , p 4 ) such that changes to P i are proportional to changes in the electrochemical potential of dot i.…”

“…The nonlinear and nonlocal dependence of exchange couplings on the barrier-gate voltages, which results from position shifts of the quantum dots, poses a challenge to implementing simultaneous exchange coupling between all dots in an extended array. Previous work has investigated how to adjust multiple interdot tunnel couplings iteratively [26,30]. Here, we discuss two different models that allow us to determine the virtual-gate voltages given a set of target exchange couplings.…”

“…Partly because of these exciting possibilities, independent and automated control of interdot tunnel couplings has been the focus of intense research in quantum-dot arrays [26][27][28][29][30][31]. However, generating multiple independent, nonzero, and coherent exchange couplings in quantum-dot arrays is challenging for many reasons.…”

Machine learning enables completely automatic tuning of a quantum device faster than human experts

“…Moreover, the transport features that indicate the device is tuned as a double quantum dot are time-consuming to measure and difficult to parametrise. Machine learning techniques and other automated approaches have been used for tuning quantum devices [5][6][7][8][9][10][11][12][13][14] . These techniques are limited to small regions of the device parameter space or require information about the device characteristics.…”

Efficient Orthogonal Control of Tunnel Couplings in a Quantum Dot Array

“…However, the interdot tunnel coupling is approximately an exponential function of the gate voltages [10,16,17], and so far it has remained unclear how to incorporate this nonlinear dependence into the crosstalk matrix. Therefore, tuning of multiple tunnel couplings in a multidot device is mostly done by iteratively adjusting gate voltages using manual or computer-automated procedures [18,19].…”