The disparate impact of noise on quantum learning algorithms ; L'impact disparate du bruit sur les algorithmes d'apprentissage quantique
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| Název: | The disparate impact of noise on quantum learning algorithms ; L'impact disparate du bruit sur les algorithmes d'apprentissage quantique |
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| Autoři: | Angrisani, Armando |
| Přispěvatelé: | Information Quantique LIP6 (QI), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université, Elham Kashefi, Vincent Cohen-Addad |
| Zdroj: | https://theses.hal.science/tel-04511706 ; Data Structures and Algorithms [cs.DS]. Sorbonne Université, 2023. English. ⟨NNT : 2023SORUS626⟩. |
| Informace o vydavateli: | CCSD |
| Rok vydání: | 2023 |
| Témata: | Learning, Quantum, Privacy, Apprentissage, Quantique, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
| Popis: | Quantum computing, one of the most exciting scientific journeys of our time, holds remarkable potential by promising to rapidly solve computational problems. However, the practical implementation of these algorithms poses an immense challenge, with a universal and error-tolerant quantum computer remaining an elusive goal. Currently, short-term quantum devices are emerging, but they face significant limitations, including high levels of noise and limited entanglement capacity. The practical effectiveness of these devices, particularly due to quantum noise, is a subject of debate. Motivated by this situation, this thesis explores the profound impact of noise on quantum learning algorithms in three key dimensions. Firstly, it focuses on the influence of noise on variational quantum algorithms, especially quantum kernel methods. Our results reveal significant disparities between unital and non-unital noise, challenging previous conclusions on these noisy algorithms. Next, it addresses learning quantum dynamics with noisy binary measurements of the Choi-Jamiolkowski state, using quantum statistical queries. The Goldreich-Levin algorithm can be implemented in this way, and we demonstrate the efficiency of learning in our model. Finally, the thesis contributes to quantum differential privacy, demonstrating how quantum noise can enhance statistical security. A new definition of neighboring quantum states captures the structure of quantum encodings, providing stricter privacy guarantees. In the local model, we establish an equivalence between quantum statistical queries and local quantum differential privacy, with applications to tasks like asymmetric hypothesis testing. The results are illustrated by the efficient learning of parity functions in this model, compared to a classically demanding task. ; L'informatique quantique, l'un des voyages scientifiques les plus passionnants de notre époque, offre un potentiel remarquable en promettant de résoudre rapidement des problèmes computationnels. Cependant, la mise en œuvre ... |
| Druh dokumentu: | doctoral or postdoctoral thesis |
| Jazyk: | English |
| Relation: | NNT: 2023SORUS626 |
| Dostupnost: | https://theses.hal.science/tel-04511706 https://theses.hal.science/tel-04511706v1/document https://theses.hal.science/tel-04511706v1/file/140713_ANGRISANI_2023_archivage.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| Přístupové číslo: | edsbas.F1E7B6A3 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Quantum computing, one of the most exciting scientific journeys of our time, holds remarkable potential by promising to rapidly solve computational problems. However, the practical implementation of these algorithms poses an immense challenge, with a universal and error-tolerant quantum computer remaining an elusive goal. Currently, short-term quantum devices are emerging, but they face significant limitations, including high levels of noise and limited entanglement capacity. The practical effectiveness of these devices, particularly due to quantum noise, is a subject of debate. Motivated by this situation, this thesis explores the profound impact of noise on quantum learning algorithms in three key dimensions. Firstly, it focuses on the influence of noise on variational quantum algorithms, especially quantum kernel methods. Our results reveal significant disparities between unital and non-unital noise, challenging previous conclusions on these noisy algorithms. Next, it addresses learning quantum dynamics with noisy binary measurements of the Choi-Jamiolkowski state, using quantum statistical queries. The Goldreich-Levin algorithm can be implemented in this way, and we demonstrate the efficiency of learning in our model. Finally, the thesis contributes to quantum differential privacy, demonstrating how quantum noise can enhance statistical security. A new definition of neighboring quantum states captures the structure of quantum encodings, providing stricter privacy guarantees. In the local model, we establish an equivalence between quantum statistical queries and local quantum differential privacy, with applications to tasks like asymmetric hypothesis testing. The results are illustrated by the efficient learning of parity functions in this model, compared to a classically demanding task. ; L'informatique quantique, l'un des voyages scientifiques les plus passionnants de notre époque, offre un potentiel remarquable en promettant de résoudre rapidement des problèmes computationnels. Cependant, la mise en œuvre ... |
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