Achieving fast and robust perfect entangling gates via reinforcement learning

TL;DR

This paper introduces reinforcement learning methods to design fast, robust, and near-perfect entangling gates for quantum computers, reducing calibration efforts and demonstrating hardware-agnostic control strategies.

Contribution

It applies reinforcement learning to quantum control, achieving near-optimal, noise-resilient entangling gates with less calibration, advancing quantum gate design techniques.

Findings

RL-designed pulses outperform traditional methods under noise

RL approach reduces calibration overhead

Method is adaptable across different quantum hardware

Abstract

Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits. Central to realizing the potential of quantum computing are perfect entangling (PE) two-qubit gates, which serve as a critical building block for universal quantum computation. In the context of quantum optimal control, shaping electromagnetic pulses to drive quantum gates is crucial for pushing gate performance toward theoretical limits. In this work, we leverage reinforcement learning (RL) techniques to discover near-optimal pulse shapes that yield PE gates. A collection of RL agents is trained within robust simulation environments, enabling the identification of effective control strategies even under noisy conditions. Selected agents are then validated on higher-fidelity simulations, illustrating how RL-based methods can reduce calibration overhead when compared to quantum optimal control techniques. Furthermore, the RL approach is hardware agnostic with the potential for broad applicability across various quantum computing platforms.