Noise-robust classification of single-shot electron spin readouts using a deep neural network

Yuta Matsumoto,Andreas D. Wieck,Takafumi Fujita,Kazunori Komatani,Arne Ludwig,Akira Oiwa

doi:10.1038/s41534-021-00470-7

Abstract

Single-shot readout of charge and spin states by charge sensors such as quantum point contacts and quantum dots are essential technologies for the operation of semiconductor spin qubits. The fidelity of the single-shot readout depends both on experimental conditions such as signal-to-noise ratio, system temperature, and numerical parameters such as threshold values. Accurate charge sensing schemes that are robust under noisy environments are indispensable for developing a scalable fault-tolerant quantum computation architecture. In this study, we present a novel single-shot readout classification method that is robust to noises using a deep neural network (DNN). Importantly, the DNN classifier is automatically configured for spin-up and spin-down traces in any noise environment by tuning the trainable parameters using the datasets of charge transition signals experimentally obtained at a charging line. Moreover, we verify that our DNN classification is robust under noisy environment in comparison to the two conventional classification methods used for charge and spin state measurements in various quantum dot experiments.

Highlights

Electron spins in semiconductor quantum dots (QDs) are a promising candidate for quantum bits, owing to their potential scalability1,2, for fault-tolerant quantum computing
Deep learning can flexibly tune the trainable parameters through a simple procedure, depending on the noises and sensor sensitivity for event detections. This is a remarkable advantage of the deep neural network (DNN) classification; it automatically builds an appropriate algorithm for each spin qubit, which has a small signal-to-noise ratio (SNR)
We proposed and demonstrated a new method based on a DNN to classify noisy single-shot electron charge and spin readout traces

Summary

INTRODUCTION

Electron spins in semiconductor quantum dots (QDs) are a promising candidate for quantum bits (qubits), owing to their potential scalability, for fault-tolerant quantum computing. Spin qubits in QDs are measured using single-shot readouts via a spinto-charge conversion technique in which spin states are distinguished by the presence of a charge transition event in the sensor signals. Spin qubits in QDs are measured using single-shot readouts via a spinto-charge conversion technique in which spin states are distinguished by the presence of a charge transition event in the sensor signals3 Such sensors are usually located close to QDs and are capacitively coupled to QDs. For the scalability of universal quantum computation, a high fidelity (≥99%), is required for all processes including preparation, control, and measurement of single qubits to implement the surface code error correction protocol. We show the training procedure to apply the DNN classification to the real QD devices and evaluate the classification accuracy of charge transition events occurring in GaAs-based lateral QDs. we demonstrate the precise energy selective electron spin readouts using the trained DNN classifier in a noisy environment

RESULTS

DISCUSSION

METHODS

CODE AVAILABILITY