Reinforcement learning entangling operations on spin qubits

TL;DR

This paper introduces a reinforcement learning method to discover high-fidelity entangling protocols for spin qubits in quantum dots, effectively handling realistic experimental constraints and noise.

Contribution

It presents a novel RL-based approach to optimize entangling operations in semiconductor spin qubits, surpassing traditional gradient methods and addressing practical experimental challenges.

Findings

RL protocols achieve high-fidelity entangling gates

Method handles realistic noise and constraints

Outperforms traditional optimization techniques

Abstract

High-fidelity control of one- and two-qubit gates past the error correction threshold is an essential ingredient for scalable quantum computing. We present a reinforcement learning (RL) approach to find entangling protocols for semiconductor-based singlet-triplet qubits in a double quantum dot. Despite the presence of realistically modelled experimental constraints, such as various noise contributions and finite rise-time effects, we demonstrate that an RL agent can yield performative protocols, while avoiding the model-biases of traditional gradient-based methods. We optimise our RL approach for different regimes and tasks, including training from simulated process tomography reconstruction of unitary gates, and investigate the nuances of RL agent design.