This paper uses reinforcement learning and importance sampling to outperform previous work at all scales and reduces the logical error rate by 25.9\% and 71.7\% on average, respectively.
Abstract
A key subtask of quantum error correction is to extract a syndrome that, if nontrivial, signals an error. The number of possible ways to extract a syndrome grows exponentially with the syndrome size, and these implementations vary greatly in fault tolerance, as measured by their logical error rates. This creates a natural search problem: find an implementation with a low logical error rate. Previous work solves this problem but sacrifices either solution quality or scalability. In this paper, we use reinforcement learning and importance sampling to outperform previous work at all scales. Compared with the state of the art automatic scheduling tools AlphaSyndrome and PropHunt, our tool reduces the logical error rate by 25.9\% and 71.7\% on average, respectively, culminating with a reduction of 97.8\% for a surface code with distance 15.
Accurate digital quantum simulation at long times is limited by the accumulation of errors inherent to approximate simulation. Here we introduce RL-Trotter, a reinforcement-learning framework that treats unavoidable approximation errors as resources for error correction rather than merely imperfections to suppress. We...
Yu-Bo Shi, M. Heyl, R. Moessner et al.· 0 citations
One of the most important initial steps in quantum computing is high-fidelity quantum state preparation, because errors in it can affect the final computational result. Therefore, one of the major challenges is to design a quantum circuit that can achieve the desired target state accurately with a smaller number of gat...
Om Bhamare, Aryan Jadhav, Manas Shinde et al.· 0 citations
This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations.
Changwon Lee, Tak Hur, Jeongwoo Jae et al.· 0 citations
This paper trains a simulated quantum reinforcement learning model with different levels of mid-circuit measurements, and investigates its performance in the cartpole environment, finding that the used quantum reinforcement circuit has a very variable performance, which causes the performance impact of the mid-circuit...
A layout-based marginalisation fix is implemented, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.
Soraya V. Panambalom, Edoardo Altamura, Nicholas Chancellor et al.· 0 citations
This paper repurposes uniformly controlled rotations from static state preparation and static data encoding into a state-encoding interface for quantum neural networks, elevating the process of integrating classical states into quantum neural networks to an independent method-ological layer and achieves a more stable p...
Jun-Chen Han, Feng-Tao Xiang, Hao Shi et al.· Chinese Physics B· 0 citations
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