Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning
Austin Yubo He, Zi-Wen Liu
2025-02-21
Summary
This paper talks about using artificial intelligence, specifically reinforcement learning (RL), to create better quantum error-correcting codes. These codes are crucial for making quantum computers work reliably by protecting them from errors.
What's the problem?
Quantum computers are very sensitive to errors, and we need special codes to protect them. Current codes often require complex measurements that are hard to implement and can introduce more errors. We need simpler, more efficient codes, especially for near-future quantum computers that aren't very large yet.
What's the solution?
The researchers developed a new way to use reinforcement learning to design quantum error-correcting codes. Their method focuses on reducing the 'weight' of the measurements needed, which makes the codes simpler and more practical. They were able to create new codes that perform much better than existing ones, especially for smaller quantum computers that we might build in the near future.
Why it matters?
This matters because it could speed up the development of practical, fault-tolerant quantum computers. The new codes they found could reduce the number of physical qubits needed by 10 to 100 times compared to current methods. This makes it much more feasible to build useful quantum computers sooner. It also shows that AI can be a powerful tool for solving complex problems in quantum computing, potentially leading to more breakthroughs in the future.
Abstract
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.