Machine Learning for Ground State Preparation via Measurement and Feedback

TL;DR

This paper introduces a recurrent neural network approach that uses measurement and feedback to efficiently prepare ground states in quantum systems, outperforming previous methods by dynamically adjusting circuits based on real-time data.

Contribution

It presents a novel machine learning method that leverages mid-circuit measurement and feedback for ground state preparation, learning distinct protocols for different Hamiltonians.

Findings

Performance improves with more ancilla qubits used for measurement and feedback.

The algorithm consistently finds a strategy involving an intermediate state before the ground state.

Mid-circuit measurements significantly enhance state preparation efficiency.

Abstract

We present a recurrent neural network-based approach for ground state preparation utilizing mid-circuit measurement and feedback. Unlike previous methods that use machine learning solely as an optimizer, our approach dynamically adjusts quantum circuits based on real-time measurement outcomes and learns distinct preparation protocols for different Hamiltonians. Notably, our machine learning algorithm consistently identifies a state preparation strategy wherein all initial states are first steered toward an intermediate state before transitioning to the target ground state. We demonstrate that performance systematically improves as a larger fraction of ancilla qubits are utilized for measurement and feedback, highlighting the efficacy of mid-circuit measurements in state preparation tasks.