Evidence map›Paper›PMID 39879301›Full record

ArticleScience advances2025

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

Shi-An Guo, Yu-Kai Wu, Jing Ye, Lin Zhang, Ye Wang, Wen-Qian Lian, Rui Yao, Yu-Lin Xu, Chi Zhang, Yu-Zi Xu and 5 more

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shi-An GuoCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0001-6393-6306
Yu-Kai WuCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0003-2184-7553
Jing YeCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0009-0000-1087-0294
Lin ZhangCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.
Ye WangHYQ Co. Ltd., Beijing 100176, PR China.ORCID 0000-0002-4453-9417
Wen-Qian LianHYQ Co. Ltd., Beijing 100176, PR China.
Rui YaoHYQ Co. Ltd., Beijing 100176, PR China.
Yu-Lin XuCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.
Chi ZhangHYQ Co. Ltd., Beijing 100176, PR China.ORCID 0000-0003-2053-8505
Yu-Zi XuCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.
Bin-Xiang QiCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0001-5870-5247
Pan-Yu HouCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0002-6429-4488
Li HeCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.
Zi-Chao ZhouCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0002-3981-7726
Lu-Ming DuanCenter for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, PR China.ORCID 0000-0001-6675-9413

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID39879301
PMCPMC11777192

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