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Variational Quantum Eigensolvers with Quantum Gaussian Filters for solving ground-state problems in quantum many-body systems

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Yihao Liu, Min-Quan He, Z. D. Wang

Quantum Physics · Submitted 24 Jan 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Demonstrated on the Transverse Field Ising models, our method shows improved convergence speed and accuracy, particularly under noisy conditions, compared to conventional VQE methods.

how it is stated · asserted 1.00 read the record →

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arXiv ID2401.13459 (version 2)
Pith IDTJ7CZBNI, Pith's permanent record of this paper and its review
CitationMin-Quan He, Yihao Liu, Z. D. Wang. "Variational Quantum Eigensolvers with Quantum Gaussian Filters for solving ground-state problems in quantum many-body systems." arXiv:2401.13459 (2024). https://pith.science/paper/TJ7CZBNI
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,685 input tokens.

What the abstract itself states, as probabilities: claims first 0.15 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.82 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.95 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems, particularly suited for Noisy Intermediate-Scale Quantum (NISQ) devices. Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF), utilizing an iterative methodology that discretizes the application of the QGF operator into small, optimized steps through VQE. Demonstrated on the Transverse Field Ising models, our method shows improved convergence speed and accuracy, particularly under noisy conditions, compared to conventional VQE methods. This advancement highlights the potential of our algorithm in effectively addressing complex quantum simulations, marking a significant stride in quantum computing applications within the NISQ era.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems, particularly suited for Noisy Intermediate-Scale Quantum (NISQ) devices. contribution 1.00 0.85 0.11 asserted 1.00
s1 Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF), utilizing an iterative methodology that discretizes the application of the QGF operator into small, optimized steps through VQE. method 0.98 0.81 0.09 asserted 1.00
s2 Demonstrated on the Transverse Field Ising models, our method shows improved convergence speed and accuracy, particularly under noisy conditions, compared to conventional VQE methods. result 1.00 0.95 0.60 asserted 1.00
s3 This advancement highlights the potential of our algorithm in effectively addressing complex quantum simulations, marking a significant stride in quantum computing applications within the NISQ era. interpretation 1.00 0.24 0.46 hedged possibility 0.57

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.99.

What was examined, and what was not

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  • figures and tables
  • references and citing sentences
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems, particularly suited for Noisy Intermediate-Scale Quantum (NISQ) devices. Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF), utilizing an iterative methodology that discretizes the application of the QGF operator into small, optimized steps through VQE. Demonstrated on the Transverse Field Ising models, our method shows improved convergence speed and accuracy, particularly under noisy conditions, compared to conventional VQE methods. This advancement highlights the potential of our algorithm in effectively addressing complex quantum simulations, marking a significant stride in quantum computing applications within the NISQ era.

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How the literature uses it

1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper
  • Variational quantum eigensolvers (VQEs) are a type of VQA that can be used to find the ground state energy of a quantum many-body system [ref] or a chemical molecule [ref] .

    Quantum Graph Optimization Algorithm · Variational Quantum Algorithm · 0.97

What it cites · 3 sentences without a confident label

Uses the method or tool of 1 paper
  • Classical optimization techniques such as gradient descent and the ADAM optimizer [ref] are utilized in the training process, with the Hadamard test [ref] employed for estimating components of the cost function.

    Adam: A Method for Stochastic Optimization · VQE Implementation for Step Evolution · 1.00

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