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| Hauptseite > Publikationsdatenbank > Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning > print |
| Hauptseite > Publikationsdatenbank > Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning > print |
| 001 | 1038567 | ||
| 005 | 20250131215342.0 | ||
| 024 | 7 | _ | |a arXiv:2402.17761 |2 arXiv |
| 037 | _ | _ | |a FZJ-2025-01550 |
| 088 | _ | _ | |a arXiv:2402.17761 |2 arXiv |
| 100 | 1 | _ | |a Zen, Remmy |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1738316176_12726 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 500 | _ | _ | |a 34 pages, 20 figures |
| 520 | _ | _ | |a The realization of large-scale quantum computers requires not only quantum error correction (QEC) but also fault-tolerant operations to handle errors that propagate into harmful errors. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a fault-tolerant quantum circuit with flag-based protocols, especially when we consider hardware constraints, such as qubit connectivity and available gate set. In this work, we propose and explore reinforcement learning (RL) to automatically discover compact and hardware-adapted fault-tolerant quantum circuits. We show that in the task of fault-tolerant logical state preparation, RL discovers circuits with fewer gates and ancillary qubits than published results without and with hardware constraints of up to 15 physical qubits. Furthermore, RL allows for straightforward exploration of different qubit connectivities and the use of transfer learning to accelerate the discovery. More generally, our work opens the door towards the use of RL for the discovery of fault-tolerant quantum circuits for addressing tasks beyond state preparation, including magic state preparation, logical gate synthesis, or syndrome measurement. |
| 536 | _ | _ | |a 5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522) |0 G:(DE-HGF)POF4-5221 |c POF4-522 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to arXivarXiv |
| 700 | 1 | _ | |a Olle, Jan |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Colmenarez, Luis |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Puviani, Matteo |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Müller, Markus |0 P:(DE-Juel1)204218 |b 4 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Marquardt, Florian |0 P:(DE-HGF)0 |b 5 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1038567 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)204218 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-522 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Quantum Computing |9 G:(DE-HGF)POF4-5221 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-2-20110106 |k PGI-2 |l Theoretische Nanoelektronik |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-2-20110106 |
| 980 | _ | _ | |a UNRESTRICTED |
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