Two-qubit CZ gates robust against charge noise in silicon while compensating for crosstalk using neural network

TL;DR

This paper introduces a neural network-optimized composite pulse sequence for two-qubit CZ gates in silicon spin qubits, significantly improving robustness against charge noise and crosstalk, enhancing gate fidelity.

Contribution

The work presents a novel neural network-based optimization method for composite pulses that compensates for charge noise and crosstalk in silicon spin qubit gates.

Findings

Up to tenfold fidelity improvement in charge noise scenarios.

Effective crosstalk compensation using neural network-optimized pulses.

Robust gate performance in experimentally motivated conditions.

Abstract

The fidelity of two-qubit gates using silicon spin qubits is limited by charge noise. When attempting to dynamically compensate for charge noise using local echo pulses, crosstalk can cause complications. We present a method of using a deep neural network to optimize the components of an analytically designed composite pulse sequence, resulting in a two-qubit gate robust against charge noise errors while also taking crosstalk into account. We analyze two experimentally motivated scenarios. For a scenario with strong EDSR driving and negligible crosstalk, the composite pulse sequence yields up to an order of magnitude improvement over a simple cosine pulse. In a scenario with moderate ESR driving and appreciable crosstalk such that simple analytical control fields are not effective, optimization using the neural network approach allows one to maintain order-of-magnitude improvement despite the crosstalk.