Hamiltonian learning for 300 trapped ion qubits with long-range couplings

TL;DR

This paper demonstrates efficient Hamiltonian learning for a 300-qubit ion trap quantum simulator, enabling exploration of complex quantum many-body models with scalable quantum resources.

Contribution

It introduces a scalable method for learning all-to-all coupled Hamiltonians in large ion trap systems using global control and single-qubit detection.

Findings

Successfully learned a 2D ion trap Hamiltonian with 300 qubits

Achieved linear scaling of quantum resources with qubit number

Paved the way for large-scale quantum simulation applications

Abstract

Quantum simulators with hundreds of qubits and engineerable Hamiltonians have the potential to explore quantum many-body models that are intractable for classical computers. However, learning the simulated Hamiltonian, a prerequisite for any applications of a quantum simulator, remains an outstanding challenge due to the fast increasing time cost with the qubit number and the lack of high-fidelity universal gate operations in the noisy intermediate-scale quantum era. Here we demonstrate the Hamiltonian learning of a two-dimensional ion trap quantum simulator with 300 qubits. We employ global manipulations and single-qubit-resolved state detection to efficiently learn the all-to-all-coupled Ising model Hamiltonian, with the required quantum resources scaling at most linearly with the qubit number. Our work paves the way for wide applications of large-scale ion trap quantum simulators.