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- W4301136246 abstract "Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computation. Our paper adds to the literature in two ways. First, we show how von Neumann quantum measurement can be used to simulate machine learning algorithms such as Markov chain Monte Carlo (MCMC) and Deep Learning (DL) that are fundamental to Bayesian learning. Second, we describe data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Our goal then is to show how to apply quantum algorithms directly to statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes (Q-GP) and stochastic gradient descent (Q-SGD). On the empirical side, we apply a Quantum FFT model to Chicago housing data. Finally, we conclude with directions for future research." @default.
- W4301136246 created "2022-10-04" @default.
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- W4301136246 date "2022-08-17" @default.
- W4301136246 modified "2023-09-24" @default.
- W4301136246 title "Quantum Bayesian Computation" @default.
- W4301136246 doi "https://doi.org/10.48550/arxiv.2208.08068" @default.
- W4301136246 hasPublicationYear "2022" @default.
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