# Boltzmann sampling with quantum annealers via fast Stein correction

https://mdr.nims.go.jp/datasets/a25453bb-0b6f-4365-8fec-3d99a1ce0438

## File

- [PhysRevResearch.6.043050.pdf](https://mdr.nims.go.jp/filesets/9c9ef4ff-ebed-4375-bf0f-4edf8e82ba9d/download) ([Detail](https://mdr.nims.go.jp/filesets/9c9ef4ff-ebed-4375-bf0f-4edf8e82ba9d.md))

## Id

a25453bb-0b6f-4365-8fec-3d99a1ce0438

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-25T04:58:48.840628Z

## Updated at

2024-10-25T07:30:56.844465Z

## Published at

2024-10-25T07:30:56.915129Z

## Doi



## First published url

https://doi.org/10.1103/physrevresearch.6.043050

## Date published

2024-10-21

## Recorded date published

2024-10

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Boltzmann sampling with quantum annealers via fast Stein correction
  title_type: original
  lang: en

## Description

- description: Despite the attempts to apply a quantum annealer to Boltzmann sampling,
    it is still impossible to perform accurate sampling at arbitrary temperatures.
    Conventional distribution correction methods such as importance sampling and resampling
    cannot be applied, because the analytical expression of sampling distribution
    is unknown for a quantum annealer. Stein correction (Liu and Lee, 2017) can correct
    the samples by weighting without the knowledge of the sampling distribution, but
    the naive implementation requires the solution of a large-scale quadratic program,
    hampering usage in practical problems. In this letter, a fast and approximate
    method based on random feature map and exponentiated gradient updates is developed
    to compute the sample weights, and used to correct the samples generated by D-Wave
    quantum annealers. In benchmarking problems, it is observed that the residual
    error of thermal average calculations is reduced significantly. If combined with
    our method, quantum annealers may emerge as a viable alternative to long-established
    Markov chain Monte Carlo methods.
  description_type: abstract
  lang: und

## Creator

- name: Ryosuke Shibukawa
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606

## Contact agent



## Publisher

organization: American Physical Society (APS)

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## Keyword

- subject: Boltzmann sampling
  schema: not_defined
- subject: quantum annealer
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Journal

- title: Physical Review Research
  issn: '26431564'
  volume: '6'
  issue: '4'
  article_number: '043050'

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## Funding

- identifier: JPMJCR21U2
  funder_name: Japan Science and Technology Agency
- identifier: JPMJCR21O2
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJER1903
  funder_name: Exploratory Research for Advanced Technology
- identifier: JPMXP1122712807
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: 19H05819
  funder_name: Japan Society for the Promotion of Science

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- id: 9c9ef4ff-ebed-4375-bf0f-4edf8e82ba9d
  filename: PhysRevResearch.6.043050.pdf
  content_type: application/pdf
  size: 895541
  md5: c2d1d3c21999491b8c584e001dcff648

## Thumbnail

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filename: PhysRevResearch.6.043050.pdf