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- W3134339438 abstract "Quantum machine learning (QML) offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for QML. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases." @default.
- W3134339438 created "2021-03-15" @default.
- W3134339438 creator A5022166378 @default.
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- W3134339438 date "2021-03-11" @default.
- W3134339438 modified "2023-09-25" @default.
- W3134339438 title "A semi-agnostic ansatz with variable structure for quantum machine learning" @default.
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- W3134339438 doi "https://doi.org/10.48550/arxiv.2103.06712" @default.
- W3134339438 hasPublicationYear "2021" @default.
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