Autonomous estimation of high-dimensional Coulomb diamonds from sparse measurements

TL;DR

This paper introduces an autonomous method using reflectometry and learning algorithms to efficiently estimate Coulomb blockade boundaries in high-dimensional quantum dot arrays, reducing measurement complexity.

Contribution

It presents a novel hardware-triggered detection technique combined with an autonomous algorithm for high-dimensional Coulomb diamond estimation in quantum dot arrays.

Findings

Successfully demonstrated in a 2x2 silicon quantum dot array.

Accurately estimates Coulomb blockade boundary polytopes.

Reduces the number of measurements needed for high-dimensional arrays.

Abstract

Quantum dot arrays possess ground states governed by Coulomb energies, utilized prominently by singly occupied quantum dots, each implementing a spin qubit. For such quantum processors, the controlled transitions between ground states are of operational significance, as these allow the control of quantum information within the array such as qubit initialization and entangling gates. For few-dot arrays, ground states are traditionally mapped out by performing dense raster-scan measurements in control-voltage space. These become impractical for larger arrays due to the large number of measurements needed to sample the high-dimensional gate-voltage hypercube and the comparatively little information extracted. We develop a hardware-triggered detection method based on reflectometry, to acquire measurements directly corresponding to transitions between ground states. These measurements are distributed sparsely within the high-dimensional voltage space by executing line searches proposed by a learning algorithm. Our autonomous software-hardware algorithm accurately estimates the polytope of Coulomb blockade boundaries, experimentally demonstrated in a 2$\times$2 array of silicon quantum dots.