Jan. 31, 2024, 4:46 p.m. | Sahil Gulania, Yuri Alexeev, Stephen K. Gray, Bo Peng, Niranjan Govind

cs.LG updates on arXiv.org arxiv.org

Quantum computing shows great potential, but errors pose a significant
challenge. This study explores new strategies for mitigating quantum errors
using artificial neural networks (ANN) and the Yang-Baxter equation (YBE).
Unlike traditional error correction methods, which are computationally
intensive, we investigate artificial error mitigation. The manuscript
introduces the basics of quantum error sources and explores the potential of
using classical computation for error mitigation. The Yang-Baxter equation
plays a crucial role, allowing us to compress time dynamics simulations into
constant-depth …

ann artificial artificial neural networks arxiv challenge computing equation error error correction errors network networks neural network neural networks quant quant-ph quantum quantum computing shows strategies study yang

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