Efficient use of quantum computers for collider physics Article Swipe
A bstract Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.
Related Topics
Concepts
Observable
Physics
Factorization
Degrees of freedom (physics and chemistry)
Particle physics
Variety (cybernetics)
Collider
Physics beyond the Standard Model
Theoretical physics
Asymptotic freedom
Quantum
Statistical physics
Large Hadron Collider
Matrix (chemical analysis)
Supersymmetry
Quantum mechanics
Quantum computer
Bibliography
Quantum field theory
Computation
Unobservable
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/jhep11(2025)108
- https://link.springer.com/content/pdf/10.1007/JHEP11(2025)108.pdf
- OA Status
- diamond
- References
- 91
- OpenAlex ID
- https://openalex.org/W7106030308
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7106030308Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/jhep11(2025)108Digital Object Identifier
- Title
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Efficient use of quantum computers for collider physicsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-18Full publication date if available
- Authors
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Christian W. BauerList of authors in order
- Landing page
-
https://doi.org/10.1007/jhep11(2025)108Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/JHEP11(2025)108.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/JHEP11(2025)108.pdfDirect OA link when available
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Observable, Physics, Factorization, Degrees of freedom (physics and chemistry), Particle physics, Variety (cybernetics), Collider, Physics beyond the Standard Model, Theoretical physics, Asymptotic freedom, Quantum, Statistical physics, Large Hadron Collider, Matrix (chemical analysis), Supersymmetry, Quantum mechanics, Quantum computer, Bibliography, Quantum field theory, Computation, UnobservableTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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91Number of works referenced by this work
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| abstract_inverted_index.that | 57, 64, 118, 139 |
| abstract_inverted_index.then | 128 |
| abstract_inverted_index.this | 135 |
| abstract_inverted_index.used | 129 |
| abstract_inverted_index.wide | 11 |
| abstract_inverted_index.will | 155 |
| abstract_inverted_index.with | 162 |
| abstract_inverted_index.allow | 156 |
| abstract_inverted_index.argue | 138 |
| abstract_inverted_index.first | 102, 153 |
| abstract_inverted_index.other | 132 |
| abstract_inverted_index.paper | 136 |
| abstract_inverted_index.sense | 117 |
| abstract_inverted_index.short | 27, 85 |
| abstract_inverted_index.those | 63 |
| abstract_inverted_index.using | 33 |
| abstract_inverted_index.which | 109 |
| abstract_inverted_index.while | 36 |
| abstract_inverted_index.future | 141 |
| abstract_inverted_index.matrix | 150 |
| abstract_inverted_index.pieces | 56, 72 |
| abstract_inverted_index.strong | 22 |
| abstract_inverted_index.Without | 93 |
| abstract_inverted_index.ability | 95, 142 |
| abstract_inverted_index.bstract | 1 |
| abstract_inverted_index.compute | 97 |
| abstract_inverted_index.extract | 83 |
| abstract_inverted_index.freedom | 19 |
| abstract_inverted_index.involve | 7 |
| abstract_inverted_index.nature. | 43 |
| abstract_inverted_index.physics | 8, 25, 39 |
| abstract_inverted_index.predict | 131 |
| abstract_inverted_index.quantum | 145 |
| abstract_inverted_index.require | 65 |
| abstract_inverted_index.scales, | 51 |
| abstract_inverted_index.scales. | 15 |
| abstract_inverted_index.variety | 12 |
| abstract_inverted_index.allowing | 52 |
| abstract_inverted_index.distance | 14, 38, 86 |
| abstract_inverted_index.effects, | 87 |
| abstract_inverted_index.elements | 151 |
| abstract_inverted_index.identify | 54 |
| abstract_inverted_index.particle | 5 |
| abstract_inverted_index.physics. | 92, 166 |
| abstract_inverted_index.possible | 90 |
| abstract_inverted_index.reliably | 32, 75 |
| abstract_inverted_index.separate | 46 |
| abstract_inverted_index.theorems | 45 |
| abstract_inverted_index.calculate | 148 |
| abstract_inverted_index.colliders | 6 |
| abstract_inverted_index.computers | 146 |
| abstract_inverted_index.different | 50 |
| abstract_inverted_index.distances | 28 |
| abstract_inverted_index.extracted | 122 |
| abstract_inverted_index.sensitive | 88 |
| abstract_inverted_index.typically | 105 |
| abstract_inverted_index.universal | 114 |
| abstract_inverted_index.asymptotic | 18 |
| abstract_inverted_index.calculated | 31 |
| abstract_inverted_index.determined | 60 |
| abstract_inverted_index.identifies | 106 |
| abstract_inverted_index.principles | 103, 154 |
| abstract_inverted_index.determined, | 76 |
| abstract_inverted_index.information | 112 |
| abstract_inverted_index.ingredients | 100 |
| abstract_inverted_index.observables | 3, 107, 126, 161 |
| abstract_inverted_index.predictions | 159 |
| abstract_inverted_index.techniques, | 35 |
| abstract_inverted_index.experimental | 80, 125 |
| abstract_inverted_index.information, | 67 |
| abstract_inverted_index.interaction, | 23 |
| abstract_inverted_index.measurements | 81 |
| abstract_inverted_index.observables. | 133 |
| abstract_inverted_index.perturbative | 34 |
| abstract_inverted_index.Factorization | 44 |
| abstract_inverted_index.contributions | 48 |
| abstract_inverted_index.long-distance | 165 |
| abstract_inverted_index.non-universal | 163 |
| abstract_inverted_index.perturbatively | 61 |
| abstract_inverted_index.non-perturbative | 41, 66, 71, 99, 111, 149, 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.80958329 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |