This work investigates the use of reinforcement learning to perform QEC on a rotated surface code as a partially observable Markov decision process (POMDP), and finds that the tabular agent is limited by the exponential growth of the history-including state-action space, whereas PPO is able to generalize across similar observations and therefore make better use of the additional information to increase the survival length before a logical error occurs.
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
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.
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
Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time and memory cost of differentiating through an $n$-qubit, $L$-layer circuit. We propose RLQ-G...
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
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
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