Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent
Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-024-00920-y |
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| _version_ | 1849220637824909312 |
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| author | Jan Olle Remmy Zen Matteo Puviani Florian Marquardt |
| author_facet | Jan Olle Remmy Zen Matteo Puviani Florian Marquardt |
| author_sort | Jan Olle |
| collection | DOAJ |
| description | Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest. |
| format | Article |
| id | doaj-art-30d516db5e0c49b9aebae133dd2a4867 |
| institution | Kabale University |
| issn | 2056-6387 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj-art-30d516db5e0c49b9aebae133dd2a48672024-12-08T12:39:13ZengNature Portfolionpj Quantum Information2056-63872024-12-0110111710.1038/s41534-024-00920-ySimultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agentJan Olle0Remmy Zen1Matteo Puviani2Florian Marquardt3Max Planck Institute for the Science of LightMax Planck Institute for the Science of LightMax Planck Institute for the Science of LightMax Planck Institute for the Science of LightAbstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.https://doi.org/10.1038/s41534-024-00920-y |
| spellingShingle | Jan Olle Remmy Zen Matteo Puviani Florian Marquardt Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent npj Quantum Information |
| title | Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent |
| title_full | Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent |
| title_fullStr | Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent |
| title_full_unstemmed | Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent |
| title_short | Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent |
| title_sort | simultaneous discovery of quantum error correction codes and encoders with a noise aware reinforcement learning agent |
| url | https://doi.org/10.1038/s41534-024-00920-y |
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