Biased estimator channels for classical shadows Article Swipe
Zhenyu Cai
,
Adrian Chapman
,
Hamza Jnane
,
Bálint Koczor
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physreva.111.l030402
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1103/physreva.111.l030402
Extracting classical information from quantum systems is of fundamental importance, and classical shadows allow us to extract a large amount of information using relatively few measurements. Conventional shadow estimators are unbiased and thus agree with the true mean in expectation. In this Letter, we consider a biased scheme, intentionally introducing a bias in the expectation value by rescaling the conventional classical-shadow estimators to reduce the error in the finite-sample regime. The approach is straightforward to implement and requires no quantum resources. We analytically prove average-case as well as worst- and best-case scenarios, and rigorously prove that it is, in principle, always worth biasing the estimators. We illustrate our approach in a quantum simulation task of a 12-qubit spin-ring problem and demonstrate how estimating expected values of nonlocal perturbations can be significantly more efficient using our biased scheme.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physreva.111.l030402
- http://link.aps.org/pdf/10.1103/PhysRevA.111.L030402
- OA Status
- hybrid
- Cited By
- 1
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408695651
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https://openalex.org/W4408695651Canonical identifier for this work in OpenAlex
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https://doi.org/10.1103/physreva.111.l030402Digital Object Identifier
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Biased estimator channels for classical shadowsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-21Full publication date if available
- Authors
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Zhenyu Cai, Adrian Chapman, Hamza Jnane, Bálint KoczorList of authors in order
- Landing page
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https://doi.org/10.1103/physreva.111.l030402Publisher landing page
- PDF URL
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https://link.aps.org/pdf/10.1103/PhysRevA.111.L030402Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.aps.org/pdf/10.1103/PhysRevA.111.L030402Direct OA link when available
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Estimator, Mathematics, Computer science, StatisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.always | 100 |
| abstract_inverted_index.amount | 19 |
| abstract_inverted_index.biased | 46, 135 |
| abstract_inverted_index.reduce | 63 |
| abstract_inverted_index.shadow | 27 |
| abstract_inverted_index.values | 124 |
| abstract_inverted_index.worst- | 88 |
| abstract_inverted_index.Letter, | 42 |
| abstract_inverted_index.biasing | 102 |
| abstract_inverted_index.extract | 16 |
| abstract_inverted_index.problem | 118 |
| abstract_inverted_index.quantum | 4, 79, 111 |
| abstract_inverted_index.regime. | 69 |
| abstract_inverted_index.scheme, | 47 |
| abstract_inverted_index.scheme. | 136 |
| abstract_inverted_index.shadows | 12 |
| abstract_inverted_index.systems | 5 |
| abstract_inverted_index.12-qubit | 116 |
| abstract_inverted_index.approach | 71, 108 |
| abstract_inverted_index.consider | 44 |
| abstract_inverted_index.expected | 123 |
| abstract_inverted_index.nonlocal | 126 |
| abstract_inverted_index.requires | 77 |
| abstract_inverted_index.unbiased | 30 |
| abstract_inverted_index.best-case | 90 |
| abstract_inverted_index.classical | 1, 11 |
| abstract_inverted_index.efficient | 132 |
| abstract_inverted_index.implement | 75 |
| abstract_inverted_index.rescaling | 57 |
| abstract_inverted_index.spin-ring | 117 |
| abstract_inverted_index.Extracting | 0 |
| abstract_inverted_index.estimating | 122 |
| abstract_inverted_index.estimators | 28, 61 |
| abstract_inverted_index.illustrate | 106 |
| abstract_inverted_index.principle, | 99 |
| abstract_inverted_index.relatively | 23 |
| abstract_inverted_index.resources. | 80 |
| abstract_inverted_index.rigorously | 93 |
| abstract_inverted_index.scenarios, | 91 |
| abstract_inverted_index.simulation | 112 |
| abstract_inverted_index.demonstrate | 120 |
| abstract_inverted_index.estimators. | 104 |
| abstract_inverted_index.expectation | 54 |
| abstract_inverted_index.fundamental | 8 |
| abstract_inverted_index.importance, | 9 |
| abstract_inverted_index.information | 2, 21 |
| abstract_inverted_index.introducing | 49 |
| abstract_inverted_index.Conventional | 26 |
| abstract_inverted_index.analytically | 82 |
| abstract_inverted_index.average-case | 84 |
| abstract_inverted_index.conventional | 59 |
| abstract_inverted_index.expectation. | 39 |
| abstract_inverted_index.finite-sample | 68 |
| abstract_inverted_index.intentionally | 48 |
| abstract_inverted_index.measurements. | 25 |
| abstract_inverted_index.perturbations | 127 |
| abstract_inverted_index.significantly | 130 |
| abstract_inverted_index.straightforward | 73 |
| abstract_inverted_index.classical-shadow | 60 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.8679661 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |