Computational study demonstrates transformer-generated quantum circuits improve noise resilience in amplitude encoding, suggesting clearer pathways toward practical quantum simulation.
Quantum data encoding is a crucial technique for tackling practical problems with quantum computers, but achieving accurate encoding has been hindered by noise in real-world quantum devices. In this study, the authors develop a large language model to generate quantum operations for data encoding. Their results show that some of these generated operations are more resilient to noise than traditional methods, offering new hope for the realization of practical computations such as quantum chemistry simulations and quantum machine learning.
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Daimon et al. (2024) studied this question.
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