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

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 quantitative 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 use 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. We further demonstrate a physically guided learning scheme with the quantum sample complexity independent of system sizes by carefully fitting the anharmonic trap potential. Our work paves the way for wide applications of large-scale ion trap quantum simulators.

Authors

Institutions

Publication Details

Journal
Science Advances
Published
2025-01-29
DOI
https://doi.org/10.1126/sciadv.adt4713
Citations
3
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
7.44
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

C. Zhang, Shian Guo, B. Qi, Yuling Xu et al.
3 citations
Science Advances
Quantum Computing Algorithms and Architecture
7.44
article

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

C. Zhang, Shian Guo, B. Qi, Yuling Xu, J. Ye, Luming Duan, Pan‐Yu Hou, Yukai Wu, Wentao Lian, Z.-C. Zhou, Yulin Xu, Lin Zhang, Rui Yao, Y. Wang, Li He
article en
3 citations

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 quantitative 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 use 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. We further demonstrate a physically guided learning scheme with the quantum sample complexity independent of system sizes by carefully fitting the anharmonic trap potential. Our work paves the way for wide applications of large-scale ion trap quantum simulators.

Science AdvancesVol. 11(5)
Cornerstone University (US), Hefei University (CN), Beijing Founder Electronics (China) (CN), ShangHai JiAi Genetics & IVF Institute (CN), Corner House (GB), Tsinghua University (CN)
Openalex Percentile: Top 4%
Quantum Computing Algorithms and Architecture
7.44
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.