title: Discovering highly efficient low-weight quantum error-correcting codes
    with reinforcement learning
summary: The realization of scalable fault-tolerant quantum computing is expected to
  hinge on quantum error-correcting codes. In the quest for more efficient
  quantum fault tolerance, a critical code parameter is the weight of
  measurements that extract information about errors to enable error correction:
  as higher measurement weights require higher implementation costs and introduce
  more errors, it is important in code design to optimize measurement weight.
  This underlies the surging interest in quantum low-density parity-check (qLDPC)
  codes, the study of which has primarily focused on the asymptotic
  (large-code-limit) properties. In this work, we introduce a versatile and
  computationally efficient approach to stabilizer code weight reduction based on
  reinforcement learning (RL), which produces new low-weight codes that
  substantially outperform the state of the art in practically relevant parameter
  regimes, extending significantly beyond previously accessible small distances.
  For example, our approach demonstrates savings in physical qubit overhead
  compared to existing results by 1 to 2 orders of magnitude for weight 6 codes
  and brings the overhead into a feasible range for near-future experiments. We
  also investigate the interplay between code parameters using our RL framework,
  offering new insights into the potential efficiency and power of practically
  viable coding strategies. Overall, our results demonstrate how RL can
  effectively advance the crucial yet challenging problem of quantum code
  discovery and thereby facilitate a faster path to the practical implementation
  of fault-tolerant quantum technologies.
paper_authors._id: 67b81870cc6b0136b3d84254
paper_authors.name: Austin Yubo He
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-21T06:30:16.645Z
paper_authors._id: 6530a78069751712276d60ed
paper_authors.avatarUrl: /avatars/2ef4f16d0be557ed60c11d8dcef85f6f.svg
paper_authors.fullname: Austin He
paper_authors.type: user
paper_authors.user: basil2115
paper_authors._id: 67b81870cc6b0136b3d84255
paper_authors.name: Zi-Wen Liu
paper_title: Discovering highly efficient low-weight quantum error-correcting codes
    with reinforcement learning
paper_summary: The realization of scalable fault-tolerant quantum computing is expected to
  hinge on quantum error-correcting codes. In the quest for more efficient
  quantum fault tolerance, a critical code parameter is the weight of
  measurements that extract information about errors to enable error correction:
  as higher measurement weights require higher implementation costs and introduce
  more errors, it is important in code design to optimize measurement weight.
  This underlies the surging interest in quantum low-density parity-check (qLDPC)
  codes, the study of which has primarily focused on the asymptotic
  (large-code-limit) properties. In this work, we introduce a versatile and
  computationally efficient approach to stabilizer code weight reduction based on
  reinforcement learning (RL), which produces new low-weight codes that
  substantially outperform the state of the art in practically relevant parameter
  regimes, extending significantly beyond previously accessible small distances.
  For example, our approach demonstrates savings in physical qubit overhead
  compared to existing results by 1 to 2 orders of magnitude for weight 6 codes
  and brings the overhead into a feasible range for near-future experiments. We
  also investigate the interplay between code parameters using our RL framework,
  offering new insights into the potential efficiency and power of practically
  viable coding strategies. Overall, our results demonstrate how RL can
  effectively advance the crucial yet challenging problem of quantum code
  discovery and thereby facilitate a faster path to the practical implementation
  of fault-tolerant quantum technologies.
paper_ai_keywords: quantum error-correcting codes measurement weights quantum low-density parity-check (qLDPC) codes asymptotic (large-code-limit) properties reinforcement learning (RL) stabilizer codes physical qubit overhead code parameters quantum code discovery fault-tolerant quantum technologies