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Datum
2022-02-01Typ
- Conference Paper
ETH Bibliographie
yes
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Abstract
Quantum machine learning promises great speedups over classical algorithms, but it often requires repeated computations to achieve a desired level of accuracy for its point estimates. Bayesian learning focuses more on sampling from posterior distributions than on point estimation, thus it might be more forgiving in the face of additional quantum noise. We propose a quantum algorithm for Bayesian neural network inference, drawing on recent advances in quantum deep learning, and simulate its empirical performance on several tasks. We find that already for small numbers of qubits, our algorithm approximates the true posterior well, while it does not require any repeated computations and thus fully realizes the quantum speedups. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Buchtitel
Fourth Symposium on Advances in Approximate Bayesian Inference (AABI 2022)Verlag
OpenReviewKonferenz
Organisationseinheit
09568 - Rätsch, Gunnar / Rätsch, Gunnar
Anmerkungen
Poster presented on February 1, 2022.ETH Bibliographie
yes
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