Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction

TL;DR

This paper presents a dual-capability machine learning model that accurately estimates quantum Hamiltonian parameters and predicts system dynamics, validated through simulations and experiments on NMR and superconducting quantum computers.

Contribution

It introduces a novel machine learning model capable of both estimating time-dependent Hamiltonian parameters and predicting observable evolution in quantum systems.

Findings

Successfully predicted local observable dynamics in NMR quantum computer

Accurately inferred unknown Hamiltonian parameters in superconducting quantum computer

Validated model performance through theoretical simulations and two experiments

Abstract

Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems. Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancements in quantum simulations, error correction, and control protocols. This study introduces a machine learning model with dual capabilities: it can deduce time-dependent Hamiltonian parameters from observed changes in local observables within quantum many-body systems, and it can predict the evolution of these observables based on Hamiltonian parameters. Our model's validity was confirmed through theoretical simulations across various scenarios and further validated by two experiments. Initially, the model was applied to a Nuclear Magnetic Resonance quantum computer, where it accurately predicted the dynamics of local observables. The model was then tested on a superconducting quantum computer with initially unknown Hamiltonian parameters, successfully inferring them. Our approach aims to enhance various quantum computing tasks, including parameter estimation, noise characterization, feedback processes, and quantum control optimization.