Evidence map›Paper›PMID 39926708›Full record

ArticleChemical science2025

Understanding and mitigating noise in molecular quantum linear response for spectroscopic properties on quantum computers.

Karl Michael Ziems, Erik Rosendahl Kjellgren, Stephan P A Sauer, Jacob Kongsted, Sonia Coriani

Abstract read
In one paragraph

Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

5 authors.

Karl Michael ZiemsDepartment of Chemistry, Technical University of Denmark Kemitorvet Building 207 DK-2800 Kongens Lyngby Denmark kmizi@kemi.dtu.dk.ORCID https://orcid.org/0000-0001-5369-7778
Erik Rosendahl KjellgrenDepartment of Physics, Chemistry and Pharmacy, University of Southern Denmark Campusvej 55 DK-5230 Odense Denmark.ORCID https://orcid.org/0000-0002-8489-3496
Stephan P A SauerDepartment of Chemistry, University of Copenhagen DK-2100 Copenhagen Ø Denmark.ORCID https://orcid.org/0000-0003-4812-0522
Jacob KongstedDepartment of Physics, Chemistry and Pharmacy, University of Southern Denmark Campusvej 55 DK-5230 Odense Denmark.ORCID https://orcid.org/0000-0002-7725-2164
Sonia CorianiDepartment of Chemistry, Technical University of Denmark Kemitorvet Building 207 DK-2800 Kongens Lyngby Denmark kmizi@kemi.dtu.dk.ORCID https://orcid.org/0000-0002-4487-897X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The promise of quantum computing to circumvent the exponential scaling of quantum chemistry has sparked a race to develop chemistry algorithms for quantum architecture. However, most works neglect the quantum-inherent shot noise, let alone the effect of current noisy devices. Here, we present a comprehensive study of quantum linear response (qLR) theory obtaining spectroscopic properties on simulated fault-tolerant quantum computers and present-day near-term quantum hardware. This work introduces novel metrics to analyze and predict the origins of noise in the quantum algorithm, proposes an Ansatz-based error mitigation technique, and reveals the significant impact of Pauli saving in reducing measurement costs and noise in subspace methods. Our hardware results using up to cc-pVTZ basis set serve as proof of principle for obtaining absorption spectra on quantum hardware in a general approach with the accuracy of classical multi-configurational methods. Importantly, our results exemplify that substantial improvements in hardware error rates and measurement speed are necessary to lift quantum computational chemistry from proof of concept to an actual impact in the field.

Identifiers

PMID39926708
PMCPMC11800139

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.