2023-07-05 ロスアラモス国立研究所(LANL)
◆この研究は、簡素なデータを使用することで、量子コンピュータ上での計算を容易にし、ノイズを減らして高速に実行できることを示しています。さらに、量子機械学習のアルゴリズム開発を簡素化する新しいアプローチにより、古典コンピュータにオフロードすることが可能であり、古典コンピュータでは処理しきれないような複雑な量子アルゴリズムも量子コンピュータ上で実行できます。
＜関連情報＞
- https://discover.lanl.gov/news/0705-quantum-machine-learning/
- https://www.nature.com/articles/s41467-023-39381-w
量子ダイナミクス学習のための分布外一般化 Out-of-distribution generalization for learning quantum dynamics
Matthias C. Caro>,Hsin-Yuan Huang,Nicholas Ezzell,Joe Gibbs,Andrew T. Sornborger,Lukasz Cincio,Patrick J. Coles & Zoë Holmes
Nature Communications Published:05 July 2023
DOI:https://doi.org/10.1038/s41467-023-39381-w
Abstract
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.
