001038567 001__ 1038567
001038567 005__ 20250131215342.0
001038567 0247_ $$2arXiv$$aarXiv:2402.17761
001038567 037__ $$aFZJ-2025-01550
001038567 088__ $$2arXiv$$aarXiv:2402.17761
001038567 1001_ $$0P:(DE-HGF)0$$aZen, Remmy$$b0
001038567 245__ $$aQuantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
001038567 260__ $$c2025
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001038567 3367_ $$2ORCID$$aWORKING_PAPER
001038567 3367_ $$028$$2EndNote$$aElectronic Article
001038567 3367_ $$2DRIVER$$apreprint
001038567 3367_ $$2BibTeX$$aARTICLE
001038567 3367_ $$2DataCite$$aOutput Types/Working Paper
001038567 500__ $$a34 pages, 20 figures
001038567 520__ $$aThe realization of large-scale quantum computers requires not only quantum error correction (QEC) but also fault-tolerant operations to handle errors that propagate into harmful errors. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a fault-tolerant quantum circuit with flag-based protocols, especially when we consider hardware constraints, such as qubit connectivity and available gate set. In this work, we propose and explore reinforcement learning (RL) to automatically discover compact and hardware-adapted fault-tolerant quantum circuits. We show that in the task of fault-tolerant logical state preparation, RL discovers circuits with fewer gates and ancillary qubits than published results without and with hardware constraints of up to 15 physical qubits. Furthermore, RL allows for straightforward exploration of different qubit connectivities and the use of transfer learning to accelerate the discovery. More generally, our work opens the door towards the use of RL for the discovery of fault-tolerant quantum circuits for addressing tasks beyond state preparation, including magic state preparation, logical gate synthesis, or syndrome measurement.
001038567 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001038567 588__ $$aDataset connected to arXivarXiv
001038567 7001_ $$0P:(DE-HGF)0$$aOlle, Jan$$b1
001038567 7001_ $$0P:(DE-HGF)0$$aColmenarez, Luis$$b2
001038567 7001_ $$0P:(DE-HGF)0$$aPuviani, Matteo$$b3
001038567 7001_ $$0P:(DE-Juel1)204218$$aMüller, Markus$$b4$$eCorresponding author$$ufzj
001038567 7001_ $$0P:(DE-HGF)0$$aMarquardt, Florian$$b5
001038567 909CO $$ooai:juser.fz-juelich.de:1038567$$pVDB
001038567 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)204218$$aForschungszentrum Jülich$$b4$$kFZJ
001038567 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5221$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001038567 9141_ $$y2025
001038567 920__ $$lyes
001038567 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
001038567 980__ $$apreprint
001038567 980__ $$aVDB
001038567 980__ $$aI:(DE-Juel1)PGI-2-20110106
001038567 980__ $$aUNRESTRICTED