Evidence map›Paper›PMID 37042014›Full record

ArticleNPJ quantum information2023

Towards practical and massively parallel quantum computing emulation for quantum chemistry.

Honghui Shang, Yi Fan, Li Shen, Chu Guo, Jie Liu, Xiaohui Duan, Fang Li, Zhenyu Li

Abstract read
In one paragraph

Article in NPJ quantum information, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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  6. A Vision for the Future of Multiscale Modeling.ACS physical chemistry Au · 2024
    Review
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

8 authors.

Honghui Shang *Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0003-4957-4251
Yi Fan *Hefei National Laboratory, University of Science and Technology of China, Hefei, China.
Li ShenHefei National Laboratory, University of Science and Technology of China, Hefei, China.
Chu GuoKey Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha, China.
Jie LiuHefei National Laboratory, University of Science and Technology of China, Hefei, China.ORCID 0000-0002-8140-9144
Xiaohui DuanSchool of Software, Shandong University, Jinan, China.
Fang LiNational Research Center of Parallel Computer Engineering and Technology, Beijing, China.
Zhenyu LiHefei National Laboratory, University of Science and Technology of China, Hefei, China.ORCID 0000-0003-2112-9834

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, the quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOP/s is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry.

Indexed as

Computer scienceQuantum chemistryQuantum simulation

Identifiers

PMID37042014
PMCPMC10080531

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